Systematic Mapping Study of Tools to Identify Emotions and Personality Traits

Systematic Mapping Study of Tools to Identify Emotions and Personality Traits | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Systematic Mapping Study of Tools to Identify Emotions and Personality Traits Amanul Islam, Nurul Fazmidar Binti Mod Noor, Siti Soraya Abdul Rahman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4356776/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Emotions and personality traits profoundly influence human behavior and well-being. Recent advancements in computer-based tools utilizing machine learning techniques have opened new avenues for identifying and understanding these psychological aspects in individuals. This systematic mapping study comprehensively reviews research articles from reputable journals, focusing on tools that leverage various data sources, such as text analysis, face recognition, gestures, and heart rate monitoring. The selected papers underwent rigorous analysis, leading to the categorization of identified tools based on their methodologies, objectives, and application domains. Natural language processing techniques were found to excel in capturing emotions from textual data, while deep learning models demonstrated accuracy in face recognition. Machine learning algorithms showed promise in analyzing gestures and heart rate to understand personality traits and emotional responses. However, the study also highlights the importance of validation standardization and large-scale studies across diverse populations to enhance the reliability and effectiveness of these tools. Theoretical Computer Science Systematic Mapping Study Emotion Traits Personality Traits Artificial Intelligence Machine Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 I. INTRODUCTION Emotions and personality traits are fundamental components of human behavior and play a significant role in shaping individual experiences, social interactions, and overall well-being. Understanding and accurately identifying emotions and personality traits can provide valuable insights for various fields, including psychology, human-computer interaction, healthcare, marketing, and personalized services [1]. In recent years, rapid advancements in machine learning and artificial intelligence have led to the development of sophisticated computer-based tools capable of recognizing and characterizing emotions and personality traits with increasing accuracy and efficiency [2]. These tools harness diverse data sources, such as text analysis, face recognition, gestures, and physiological signals such as heart rate monitoring, to gain a comprehensive understanding of an individual's psychological makeup [3]. Research has demonstrated the potential of natural language processing (NLP) techniques for capturing emotions from textual data, such as social media posts, online reviews, and written communication [4]. These methods utilize sentiment analysis, emotion classification, and affective computing algorithms to discern the emotional tone and underlying sentiments expressed in texts [5]. Additionally, facial expressions are vital nonverbal cues that provide valuable insights into emotions [6]. Advanced deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown impressive capabilities in accurately recognizing and classifying facial expressions, paving the way for applications in areas such as human-computer interaction, virtual reality, and emotion-aware systems [7]. Moreover, gesture recognition and physiological signals, such as heart rate, have been explored as potential indicators of personality traits and emotional responses [8]. Machine learning algorithms, such as support vector machines (SVMs), decision trees, and neural networks, have been employed to analyze patterns in gestures and physiological signals to infer an individual's personality traits and emotional states [9]. These developments have significant implications for understanding human behavior and enhancing personalized services and interactions in various domains [10]. Despite the rapid growth in this area, the field of computer-based emotion and personality identification is diverse and fragmented. A systematic mapping study is essential for gathering, categorizing, and synthesizing the literature to identify trends, highlight potential challenges, and guide future research directions. By conducting a systematic mapping study, we aim to contribute to the consolidation of knowledge and inform researchers, practitioners, and developers working on emotion and personality recognition systems [11]. This systematic mapping study will provide an overview of the current state of research on computer-based tools employing machine learning to identify emotions and personality traits. We anticipate that the findings will facilitate advancements in understanding human psychology, enabling the development of more accurate and effective tools for a range of applications in psychology, technology, healthcare, marketing, and beyond. The objective of this systematic mapping study is to conduct a comprehensive and contemporary review of the research landscape surrounding computer-based tools employing machine learning techniques for the identification of emotions and personality traits. Through an extensive literature review encompassing peer-reviewed journal papers, this study seeks to identify cutting-edge methods and trends within this rapidly evolving domain. Specifically, our aim is to elucidate the potential of natural language processing (NLP) techniques for capturing emotions from textual data, the advancements in facial expression recognition enabled by deep learning models, and the exploration of gesture recognition and physiological signals as indicators of personality traits and emotional responses. The primary objective of our study was to explore the application of machine learning techniques in the identification of emotions and personality traits. However, the initial presentation of our research questions (RQs) may have obscured the specific objectives of the study. To rectify this, we will explicitly state our objectives as follows: To investigate the effectiveness of machine learning models in identifying emotions and personality traits compared to traditional methods. To assess the scope and limitations of existing reviews in this area and identify research gaps that warrant further investigation. To provide insights into the specific reasons for conducting this study, there is a need to bridge existing knowledge gaps and contribute to advancements in the field of emotion and personality trait identification. II. BACKGROUND The field of emotion and personality research has a long history in psychology, with researchers exploring various methods to assess and understand these psychological constructs. Traditionally, psychologists have relied on self-report questionnaires, interviews, and observations to gauge emotions and personality traits. While these methods have been valuable, they often suffer from subjective biases and limitations in capturing real-time emotional states and subtle personality nuances. In recent years, the emergence of machine learning and artificial intelligence has brought about transformative changes in the field of emotion and personality identification. Researchers have increasingly turned to computer-based tools that leverage machine learning techniques to analyze vast amounts of data and make accurate inferences about an individual's emotions and personality traits. In the realm of natural language processing (NLP), sentiment analysis has gained substantial attention as a means to identify emotions from textual data. Researchers have developed sophisticated algorithms that can classify text as positive, negative, or neutral and, in some cases, even identify specific emotions such as joy, anger, sadness, or fear from written content [ 12 ]. These advancements have found practical applications in sentiment analysis for customer feedback, social media monitoring, and analyzing user interactions on various platforms. Facial expression recognition has emerged as another crucial area in computer-based emotion identification. Deep learning models, particularly convolutional neural networks (CNNs), have proven highly effective in accurately recognizing and categorizing facial expressions [ 13 ]. These models can distinguish between various emotional states displayed in facial expressions, such as happiness, surprise, disgust, and fear. Facial expression recognition has practical implications in fields such as human-computer interaction, virtual reality, and affective computing. Moreover, machine learning algorithms have been explored for analyzing gestures and physiological signals to infer personality traits and emotional states. Gesture recognition systems, powered by machine learning techniques, can capture and interpret body movements to infer an individual's personality traits, such as extraversion, openness, and agreeableness [ 14 ]. Similarly, machine learning has been applied to analyze physiological signals, such as heart rate variability, to detect and interpret emotional responses [ 15 ]. These developments open up new avenues for understanding emotions and personality traits through nonverbal cues and physiological responses. Despite these advancements, the field of computer-based emotion and personality identification remains diverse and fragmented. Studies often focus on specific data sources or limited sample sizes, which may restrict the generalizability of the findings. Additionally, the validation methods and standardization of machine learning models require further attention to ensure the reliability and effectiveness of the identified tools. Table 1 Literature Review Summary Study Data Source Machine Learning Techniques Findings and Applications Shaik et al.(2023)[ 16 ] Text Sentiment Analysis Sentiment classification in online reviews Lek et al.(2023)[ 17 ] Text Emotion Classification Fine-grained emotion labeling in text Tripathi et al. (2021)[ 18 ] Images (Faces) Convolutional Neural Networks(CNN) Accurate facial expression recognition Xu et al. (2023)[ 19 ] Images (Faces) Deep Neural Networks (DNN) Subtle emotion recognition using facial cues Vaijayanthi et al. (2022)[ 20 ] Gestures, Body Posture Random Forest Gesture recognition for emotion identification Bota et al.(2019)[ 21 ] Physiological Signals Support Vector Machines (SVM) Correlation of heart rate variability with emotions Zhang et al.(2020)[ 22 ] Text, Physiological Signals Long Short-Term Memory (LSTM) Combined text and physiology for emotion recognition Mustafa et al. (2023)[ 23 ] Images (Faces) k-Nearest Neighbors (k-NN) Cross-cultural facial expression recognition Romeo et al. (2019)[ 24 ] Physiological Signals Decision Trees Affective computing using physiological signals Wei et al. (2020)[ 25 ] Gestures, Speech Recurrent Neural Networks (RNN) Multimodal fusion for emotion recognition This systematic mapping study seeks to consolidate the existing knowledge and identify trends in the field of computer-based tools for emotion and personality identification using machine learning techniques. By reviewing and categorizing a wide range of research articles, as shown in Table 1 , this study aims to provide insights that will facilitate advancements in understanding human emotions and personality traits for various practical applications. The scope of our research encompasses the exploration of machine learning (ML) techniques in the identification of emotions and personality traits. Specifically, we aim to investigate the application of ML models in analyzing various data sources, including text, speech, facial expressions, physiological signals, and behavioral patterns, to infer emotional states and personality characteristics. Within this scope, our study focuses on the following: Algorithm Performance : We assess the effectiveness and accuracy of ML algorithms, such as neural networks, support vector machines, decision trees, and ensemble methods, in identifying emotions and personality traits. The evaluation metrics included the accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). Data Requirements : We will examine the types and quantities of data required for training ML models for emotion and personality trait identification. This includes exploring the role of feature selection, data preprocessing techniques, and data augmentation strategies in enhancing model performance. Ethical considerations : We will discuss the ethical implications of using ML techniques in this context, including issues related to data privacy, bias and fairness, interpretability of results, and potential societal impacts. We will also explore approaches for mitigating ethical concerns and promoting the responsible use of ML-based identification methods. Comparison with Traditional Methods : We will compare ML-based identification approaches with traditional methods, such as manual coding, questionnaire-based assessments, and clinical interviews, to evaluate their relative strengths, limitations, and practical implications. This comparison will provide insights into the advantages and challenges of adopting ML techniques in this domain. Application Domains : We explore the diverse application domains of ML-based emotion and personality trait identification, including mental health diagnosis and monitoring, human-computer interaction, personalized recommendations, marketing and advertising, and educational interventions. By examining these application areas, we aim to highlight the potential impact of ML-driven approaches in real-world settings. Our research seeks to contribute to a deeper understanding of the capabilities, challenges, and ethical considerations associated with using ML techniques for emotion and personality trait identification. By delineating the scope of our investigation, we aim to provide a comprehensive analysis that advances the state of the art in this rapidly evolving field. III. EMOTION AND PERSONALITY TRAITS IN COMPUTER SCIENCE In the realm of computer science, the study of emotion and personality traits has gained significant importance. These psychological constructs offer valuable insights and applications, influencing fields such as human-computer interaction, artificial intelligence, sentiment analysis, and personalized computing. Two prominent aspects explored within computer science are the Big Five Personality Traits and the Dark Triad Personality Traits [ 26 ]. The Big Five Personality Traits , also known as the Five-Factor Model, represent a widely accepted framework for characterizing human personality, as shown in Fig. 1 . Within computer science, these traits are leveraged to enhance user experiences and tailor digital interactions [ 27 ]. These five dimensions encompass the following: Openness to Experience : This trait relates to individuals who are open to novelty and creativity. In computer science, it can influence the design of innovative and user-friendly interfaces. Conscientiousness : Conscientious individuals are organized and goal-oriented. In computer science, this trait informs the development of efficient software systems and user workflows. Extraversion : Extraverted individuals are outgoing and sociable. In computer science, this trait is considered in the creation of social and collaborative platforms. Agreeableness : Agreeable individuals are cooperative and considerate. In computer science, this trait influences the design of user interfaces that promote positive interactions. Neuroticism : Neuroticism relates to emotional instability and stress susceptibility. In computer science, understanding this trait aids in developing stress-reduction applications and assessing user emotional states during interactions. These personality traits play a vital role in creating personalized systems and adaptive technologies that cater to individual preferences and emotional states. The Dark Triad Personality Traits represent a different facet of personality characterized by socially undesirable traits, as shown in Fig. 2 . Computer science has examined these traits for various purposes, including cybersecurity and fraud detection [ 28 ]. The Dark Triad comprises the following: Machiavellianism : Individuals with high Machiavellianism tend to be manipulative and strategic. In computer security, understanding Machiavellian tendencies helps identify deceptive behavior and potential threats. Narcissism : Narcissistic individuals exhibit an exaggerated sense of self-importance. Recognizing narcissistic behavior in the digital realm is crucial for addressing online harassment and cyberbullying issues. Psychopathy : Psychopathy encompasses traits such as callousness and a lack of empathy. In computer science, identifying psychopathic traits is relevant for cybersecurity and risk assessment, as psychopaths may engage in malicious online activities. While the Dark Triad traits are typically associated with negative behaviors, their study within computer science contributes to enhancing digital security and safeguarding against online threats. Emotions are a fundamental aspect of human experience, and understanding them is becoming increasingly vital in the realm of computer science. Emotion-aware computing seeks to create systems that can not only recognize but also respond to human emotions effectively. By incorporating emotional intelligence into software and applications, computer scientists can significantly enhance user experiences. For instance, sentiment analysis algorithms can gauge user emotions in social media, customer reviews, and feedback, providing valuable insights for businesses and marketers [ 29 ]. Emotion recognition in facial expressions, speech, and text allows for more empathetic chatbots and virtual assistants. Moreover, in healthcare applications, emotion-aware systems can contribute to stress management, mental health assessment, and patient care. As technology continues to advance, the ability to understand and respond to human emotions is poised to revolutionize how we interact with and benefit from computational systems [ 30 ]. Emotions and personality traits play multifaceted roles within computer science. By optimizing user experiences based on the Big Five Personality Traits to bolster cybersecurity through an understanding of the Dark Triad, these constructs shape technology and human-computer interactions [ 31 ]. Recognizing and integrating these insights will empower computer scientists to create more personalized, secure, and effective digital systems. Emotions, in particular, add a layer of complexity and depth to user interactions, making it increasingly important for computer scientists to incorporate emotional intelligence into their designs and applications [ 32 ]. Figure 3 shows a schematic of the 2D emotion model. The 2D emotion model will be used to determine the target/real/actual emotion-related data labels as the ground truth to evaluate the emotion classification accuracy. IV. METHODOLOGY A. SYSTEMATIC MAPPING PEOCESS The systematic mapping process serves as the backbone of this research, allowing for the comprehensive evaluation and organization of literature related to tools aimed at identifying emotions and personality traits [ 33 ]. In line with the distinctive characteristics of systematic mapping studies, the process encompasses distinct stages that culminate in the creation of a systematic map that encapsulates the essence of the research domain [ 34 ]. Figure 4 illustrates the sequential stages of the systematic mapping process. It commences with the formulation of research questions, which serve as guiding beacons for the subsequent steps. This process entails a meticulous search for pertinent research papers across reputable databases. Next, the papers were subjected to rigorous screening based on predefined inclusion and exclusion criteria. The abstracts are methodically analyzed, keywords are identified, and data extraction is executed to distill key information from each paper. This extracted information is then mapped, leading to the creation of a systematic map that encapsulates the overarching trends and findings observed across the literature. In the domain of software engineering, systematic mapping studies have become prevalent, with Petersen's guidelines serving as a cornerstone for conducting systematic mapping reviews [ 35 ]. This methodology's applicability extends beyond software engineering, permeating other research domains, including those that delve into emotion and personality trait identification. Notably, the fusion of Petersen's systematic mapping study guidelines with Kitchenham's systematic literature review guidelines has emerged as a best practice for conducting thorough systematic mapping studies [ 35 ][ 36 ]. This composite approach combines the strengths of both methodologies, facilitating a nuanced and comprehensive exploration of the research landscape. As shown in Fig. 1 , Kitchenham's systematic mapping study [ 35 ] comprises five pivotal phases. The formulation of the research question marks its inception, serving as the cornerstone for subsequent exploration. The delineation of a robust search strategy leads to the identification of relevant papers. Subsequent phases encompass paper screening to refine the selection and a classification scheme phase, which aids in categorizing the selected papers. The culmination of the process involves mapping these studies onto a classification scheme, often visualized through a bubble plot graph. This systematic mapping technique, depicted in Fig. 1 , serves as the cornerstone of this research's methodology. B. RESEARCH QUESTIONS Table 2 Research questions RQ Research Questions Motivation RQ1 What are the existing challenges of developing tools for identifying emotions and personality traits? This question aims to uncover the inherent challenges researchers face when creating tools to effectively identify emotions and personality traits. It sheds light on methodologies and data analysis techniques. RQ2 What are the key open issues in developing tools for identifying emotions and personality traits? This question seeks to determine whether research designs, contexts, and objectives primarily revolve around exploring the identification of emotions and personality traits. RQ3 What types of contributions have been proposed in this research field? The objective of this question is to identify the diverse range of contributions made within the realm of emotion and personality trait identification, offering insights into proposed solutions. RQ4 What is the most focal topic of the publication trend in the studies of identifying emotions and personality traits from the selected studies during the last five years? Unveiling the central topics of recent publications within the past five years allows for the identification of influential trends, journals, conferences, and geographical distributions. The research undertaken revolves around conducting a systematic mapping study aimed at comprehensively exploring the landscape of tools designed for the identification of emotions and personality traits. The systematic mapping process serves as the foundational framework, systematically guiding the research through distinct phases. Beginning with the formulation of research questions, the study embarks on an extensive search for relevant research papers across reputable databases. These papers were meticulously screened based on predefined inclusion and exclusion criteria, and abstracts were meticulously analyzed to identify pertinent keywords and concepts. Subsequently, data extraction is performed to glean essential information from each paper. This culminates in the creation of a systematic map, visually encapsulating overarching trends and findings discerned from the literature. The research questions shown in Table 2 —ranging from elucidating challenges in tool development to identifying key contributions and publication trends—guide this exploration. The study not only is anchored in Petersen's systematic mapping study guidelines but also integrates Kitchenham's systematic literature review guidelines, ensuring a comprehensive and robust methodology [ 35 ] [ 36 ]. This research aims to provide an incisive depiction of the dynamic field of tools for identifying emotions and personality traits, offering valuable insights to researchers, practitioners, and stakeholders in the interdisciplinary domains of psychology, human-computer interaction, healthcare, and more [ 37 ] [ 38 ] [ 39 ] [ 40 ]. C. CONDUCT A SEARCH FOR PRIMARY STUDIES We selected specific databases, namely, the Web of Science (WoS), ScienceDirect, IEEEXplore, and ACM Digital Library, for our literature search on emotions and personality traits, as shown in Table 3 . The Springerlink database was excluded from our search due to limited access to full electronic articles. Additionally, we encountered restrictions in accessing only a few articles on Springerlink, with only titles and abstracts available. Instead, we found that the Web of Science (WoS) database provided access to a significant portion of important and relevant publications from Springerlink. We opted not to include Google Scholar in our search because it often yields results with overlap and lower precision compared to the selected databases. Moreover, Google Scholar lacks an "advanced search" option, whereas the four electronic databases we utilized encompass the majority of relevant "high-impact" journals present in Google Scholar. Table 3 Database Study Selection Process ID Databases Initial automated search results Screened results based on relevancy Final selected studies(justifying our inclusive/exclusive criteria)) DB1 ACM Digital Library 520 265 50 DB2 IEEE Xplore 425 169 45 DB3 Science Direct 423 190 35 DB4 Web of Science 632 376 70(86 − 30 duplicates) D. SCREENING OF PAPERS FOR INCLUSION AND EXCLUSION The research team collectively developed and established clear inclusion and exclusion criteria to determine the relevance of studies in addressing our research inquiries, as shown in Table 4 . Comprehensive discussions and deliberations were held to ensure the accuracy and effectiveness of the criteria. It was deemed advantageous to exclude papers that merely briefly mentioned our primary focus, which is variability, particularly when such mentions were confined to introductory sentences within the abstracts. Table 4 Criteria for inclusion and exclusion Inc Inclusion Criteria Inc 1 We consider journal articles and conference proceedings/papers that have undergone a peer-review process. Inc 2 Articles eligible for inclusion should present substantial and relevant findings or contributions regarding the identification of emotions and personality traits in literature. These articles must clearly outline their research objectives, methodologies, and empirical results. Inc 3 Inclusion extends to articles where the central focus is on the identification of emotions and personality traits. Inc 4 In cases where multiple studies address the same topic, we prioritize the most recent research. Inc 5 The inclusion criteria cover articles published between 2019 and 2023, spanning the last decade. Exc Exclusion Criteria Exc 1 Articles not written in the English language are excluded. Exc 2 Excluded are articles classified as reviews, theoretical studies, conceptual papers, unpublished theses, and dissertations. Exc 3 Articles addressing different aspects of emotions and personality traits, such as those within psychology, are not within the scope of our inclusion. Exc 4 We exclude articles designated as "In-Press" for the year 2024. E. PROCESS OF CLASSIFICATION To establish our classification scheme, we followed the guidance provided by Petersen [ 36 ]. Our research team thoroughly examined various elements of the 200 studies that were finalized for analysis, including the title, abstract, keywords, and research methodologies employed. We also assessed the research focus, publication details such as the year of publication, and geographical distribution (country of origin). Additionally, in some cases, researchers chose to delve into other sections of the documents, such as the introduction and conclusions, to gain a comprehensive understanding of the research report's characteristics. To ensure a comprehensive classification, researchers often found it necessary to read the entire manuscript and, in some instances, closely examine figures and illustrations. We utilized a printed data extraction form to systematically record the attributes and relevant information from each paper. A visual representation of our classification methodology can be found in Fig. 5 . This process allowed us to effectively categorize and analyze the various aspects of the documents under review. V. THE RELATIONSHIP BETWEEN EMOTIONS AND PERSONALITY TRAITS We delve deeper into the intricate relationship between emotions and personality traits in the context of identification methods. Emotions and personality traits, while interconnected, represent distinct psychological constructs that influence human behavior in unique ways. Here, we explore how these constructs intersect and diverge, examining their implications for research and practice in psychology and related fields. 1. Relationship between emotion and personality trait identification : Emotions and personality traits are interconnected but distinct constructs. Emotions refer to transient, short-lived states characterized by subjective feelings and physiological responses, while personality traits represent enduring patterns of behavior, cognition, and emotion. The identification of emotions may provide valuable insights into an individual's emotional temperament, which is a component of personality. Emotional temperament refers to stable individual differences in emotional reactivity and regulation, which can influence personality traits. Conversely, personality traits can influence how individuals perceive, experience, and express emotions. For example, individuals high in neuroticism may be more prone to experiencing negative emotions, while those high in extraversion may exhibit more positive emotions. While emotions and personality traits are related, they serve different functions in understanding human behavior. Emotions provide immediate reactions to stimuli, while personality traits represent enduring characteristics that shape behavior over time. 2. Comparison of Emotion and Personality Trait Identification Methods : The methods used for identifying emotions and personality traits may differ based on the nature of the constructs and the research objectives. Emotion identification methods often rely on techniques such as facial expression analysis, physiological monitoring, self-report measures, and natural language processing to assess emotional states and reactions. In contrast, personality trait identification methods typically involve self-report questionnaires, observer ratings, behavioral observations, and standardized assessments to measure enduring patterns of behavior, cognition, and emotion. While there may be some overlap in the methods used, such as self-report measures that capture both emotions and personality traits, the emphasis and focus of these methods differ based on the construct being assessed. 3. Differentiating Emotional Phenomena in Identification Methods : Emotions encompass a range of phenomena, including emotional reactions, states, temperaments, and disorders. Each of these phenomena may require different approaches for identification. Emotional reactions refer to immediate responses to specific stimuli and may be assessed through methods such as facial expression analysis and physiological monitoring. Emotional states represent transient emotional experiences and may be measured using self-report questionnaires and ecological momentary assessment methods. Emotional temperaments are stable individual differences in emotional reactivity and regulation and may be assessed through longitudinal studies and temperament inventories. Emotional (affective) disorders, such as depression and anxiety, involve disturbances in emotional functioning and may require diagnostic interviews and clinical assessments for identification. While emotions and personality traits are related constructs, they represent distinct aspects of human psychology. Understanding the relationship between them and the methods used for their identification is essential for advancing research in this field and developing more comprehensive models of human behavior and functioning. VI. TAXONOMY OF TOOLS TO IDENTIFY EMOTIONS AND PERSONALITY TRAITS In the pursuit of understanding human emotions and personality traits through computer-based tools, it is essential to establish a taxonomy that categorizes these tools based on various criteria. This taxonomy serves as a structured framework for organizing and comprehending the diverse range of methods and technologies employed in this field, as shown in Fig. 6 . The following section outlines a taxonomy for tools used in the identification of emotions and personality traits, taking into consideration multiple dimensions that influence their classification. 1. Data Sources : The first dimension in our taxonomy categorizes tools based on the data sources they utilize for emotion and personality identification. These data sources can be broadly classified into four categories: Textual Data : Tools in this category focus on analyzing written or spoken language to extract emotional content and personality traits. Natural language processing (NLP) techniques are often employed to assess sentiment, emotion, and linguistic patterns in textual data [ 41 ]. Visual Data : This category includes tools that leverage visual cues, primarily facial expressions and body language, to infer emotions and, to some extent, personality traits. Deep learning models, such as convolutional neural networks (CNNs), are frequently used for facial expression recognition [ 42 ]. Physiological Signals : Tools in this category monitor physiological indicators such as heart rate, skin conductance, and EEG signals to infer emotions and physiological correlates of personality traits. Machine learning algorithms are applied to analyze these signals [ 43 ]. Multimodal Data : Some tools combine data from multiple sources, such as text, audio, visual, and physiological signals, to enhance the accuracy and robustness of emotion and personality identification. Multimodal fusion techniques are employed for integration and analysis [ 44 ]. 2. Machine Learning Techniques : The second dimension of our taxonomy classifies tools based on the machine learning techniques they employ. This dimension encompasses various approaches, including the following: NLP : Tools in this category predominantly use NLP techniques such as sentiment analysis, emotion classification, and linguistic pattern recognition to extract emotional and personality-related information from textual data [ 45 ]. Deep Learning : Deep learning models, including CNNs and recurrent neural networks (RNNs), are employed for tasks such as facial expression recognition, speech analysis, and multimodal data fusion, contributing to improved accuracy in emotion and personality identification [ 46 ]. Machine Learning Algorithms : This category includes traditional machine learning algorithms such as support vector machines (SVMs), decision trees, and random forests, which are utilized in various aspects of emotion and personality analysis, including gesture recognition and physiological signal analysis [ 47 ]. 3. Application Domains : The third dimension of our taxonomy focuses on the domains in which these tools find application. These domains span a wide range and include but are not limited to: Psychology : Many tools aim to contribute to psychological research by providing insights into human emotions and personality traits. These tools aid psychologists in clinical assessments, personality profiling, and understanding of emotional responses [ 48 ]. Human–Computer Interaction (HCI) : In the HCI domain, tools have been developed to create more emotionally aware and responsive systems, enhancing user experiences in applications such as virtual reality, gaming, and human–robot interaction [ 49 ]. Healthcare : Emotion and personality identification tools are applied in healthcare settings to monitor patients' emotional well-being and assess mental health conditions, such as depression and anxiety [ 50 ]. Marketing and Customer Service : Tools in this category are used to analyze consumer sentiments, preferences, and personality traits for personalized marketing strategies and improved customer service [ 51 ]. 4. Validation Methods : The final dimension in our taxonomy considers the validation methods used to assess the reliability and accuracy of the identified tools. This dimension encompasses diverse validation techniques, including self-report questionnaires, user studies, cross-validation, and benchmark datasets [ 52 ] [ 53 ] [ 54 ]. In summary, this taxonomy provides a structured framework for categorizing tools used in the identification of emotions and personality traits. It underscores the multidimensionality of this field, which encompasses various data sources, machine learning techniques, application domains, and validation methods. This taxonomy facilitates a deeper understanding of the evolving landscape of research and technology in the quest to uncover the intricacies of human emotions and personality characteristics [ 55 ]. VII. COMPARING TRADITIONAL AND COMPUTER-BASED APPROACHES TO IDENTIFYING EMOTIONS AND PERSONALITY TRAITS We delve into a comprehensive comparison between traditional and computer-based approaches for identifying emotions and personality traits, as shown in Table 5 . While the focus of our review has been primarily on the application of machine learning techniques, it is essential to acknowledge the longstanding use of traditional methods in psychological research. We aim to provide a nuanced understanding of the strengths, limitations, and suitability of both approaches. 1. Shortcomings of Traditional Methods : Traditional identification methods often rely on manual observation, self-report measures, and expert judgment. While these methods have been valuable in psychological research, they may lack the scalability, efficiency, and objectivity offered by computer-based approaches. For example, traditional methods may struggle to process large datasets or detect subtle patterns in complex emotional and personality phenomena that machine learning algorithms can effectively capture. 2. Effectiveness Comparison : Several studies have attempted to compare the effectiveness of traditional and computer-based identification methods. These comparisons typically evaluate criteria such as accuracy, reliability, scalability, and efficiency. For instance, research may compare the accuracy of human raters in identifying emotions from facial expressions with that of computer algorithms trained on large datasets. The results from such studies can provide insights into the relative strengths and weaknesses of each approach and inform decisions regarding method selection in different contexts. 3. Limitations of Machine Learning Techniques : While machine learning techniques offer significant advantages in certain contexts, they are not without limitations. One significant limitation is the reliance on labeled training data, which may introduce biases and limit the generalizability of models. Additionally, complex algorithms may lack transparency, making it challenging to interpret their decision-making processes. In cases where interpretability, transparency, or ethical considerations are paramount, researchers may prefer traditional methods that offer more straightforward and transparent approaches to identification. By thorough examination, we aim to provide researchers and practitioners with a comprehensive understanding of the strengths, weaknesses, and considerations associated with both traditional and computer-based approaches to identifying emotions and personality traits. This nuanced perspective can guide methodological choices and enhance the rigor and validity of research in this domain. Table 5 Comparison of traditional and computer-based approaches for identifying emotions and personality traits Aspect Traditional Methods Computer-Based Approaches Shortcomings -Relies on manual observation, self-report measures, and expert judgment. - Limited scalability, efficiency, and objectivity. - May lack transparency and interpretability. - Require labeled training data, which can introduce biases and limit generalizability. - Complex algorithms may be challenging to interpret. Effectiveness Comparison - Accuracy, reliability, scalability, and efficiency may vary. - May struggle to process large datasets or detect subtle patterns. - Evaluation criteria include accuracy, reliability, scalability, and efficiency. - Studies may compare human raters' accuracy with computer algorithms trained on large datasets. Limitations of Machine Learning - Relies on labeled training data, introducing biases and limiting generalizability. - Complex algorithms may lack transparency. - Transparency and interpretability may be lacking. - Dependence on labeled training data. - Complexity may hinder interpretation. - May not always align with ethical considerations or transparency requirements. This table highlights the differences between traditional and computer-based approaches for identifying emotions and personality traits, including their shortcomings, effectiveness comparison criteria, and machine learning technique limitations. VIII. RESULTS AND FINDINGS The results and findings section presents the outcomes of the systematic mapping study. It provides a comprehensive overview of the identified computer-based tools for emotion and personality identification, highlighting their methodologies, effectiveness, and application domains. Study Participants Understanding the composition and characteristics of study participants is essential for evaluating the relevance and robustness of research findings in the domain of tools designed to identify emotions and personality traits within computer science. In this section, we provide insights into the diverse range of participants involved in the studies we selected during our systematic mapping study, along with the corresponding participant numbers. The demographic information of the study participants, such as age, gender, education level, and cultural background, plays a crucial role in understanding the nuances of emotional and personality traits, as shown in Table 6 . These factors can significantly influence how individuals express emotions and manifest their personality traits in digital interactions. Our selected studies encompassed a diverse range of participant demographics, with participant numbers ranging from small cohorts of 20 to larger, more extensive samples exceeding 200[ 56 ] [ 57 ]. The size and diversity of the study sample are key considerations for the generalizability of the research findings. A larger and more diverse sample size enhances the external validity of the study results. In our selected studies, we observed variations in sample sizes. Some studies featured relatively small cohorts with approximately 30 participants, while others boasted larger, more representative samples, with participant numbers exceeding 200. Additionally, researchers often aim to include participants from various cultural backgrounds, ethnicities, and socioeconomic statuses to capture a broader range of emotional and personality variations [ 58 ]. User characteristics encompass a variety of factors, including prior experience with technology, familiarity with the specific application or tool being studied, and individual differences in cognitive and emotional traits. These characteristics can significantly influence how participants engage with technology and how their emotional responses and personality traits manifest during digital interactions. Our selected studies considered a range of user characteristics, with participant numbers correlating to the specific characteristics under investigation [ 59 ] [ 60 ]. Ethical considerations are of paramount importance in studies involving human participants. Ensuring ethical practices, including obtaining informed consent from participants, protecting their privacy and data, and minimizing potential harm, is crucial. The studies we selected, with their respective participant numbers, adhered to ethical guidelines and standards, reflecting a commitment to upholding the rights and well-being of the participants involved in the research [ 61 ]. Understanding the characteristics, demographics, and participant numbers of study participants is integral to interpreting and contextualizing research findings. It enables researchers and practitioners to assess the applicability of results to diverse user populations, ensuring that insights drawn from these studies are both meaningful and broadly relevant to the field of computer science. Understanding the characteristics, demographics, and participant numbers of study participants is integral to interpreting and contextualizing research findings. It enables researchers and practitioners to assess the applicability of results to diverse user populations, ensuring that insights drawn from these studies are both meaningful and broadly relevant to the field of computer science. Table 6 Summary of Study Participants and Study Characteristics Aspect Description Study Participants -Range of participants with numbers from 20 to over 200 [ 56 ] [ 57 ]. Sample Size -Varied, from approximately 30 to over 200, aiming for diverse demographics [ 58 ]. User Characteristics - Factors include prior tech experience and individual traits [ 59 ] [ 60 ]. Ethical Considerations - Crucial adherence to guidelines, including consent and privacy protection [ 61 ]. Table 7 presents a summary of various computerized tools developed for the assessment of emotions and personality traits. In the rapidly evolving landscape of mental health and psychological research, these innovative tools utilize a range of technologies, from natural language processing to facial recognition and physiological monitoring. Each tool serves a distinct purpose, targeting specific populations and age groups while addressing various mental health concerns. The table offers a snapshot of these tools, their applications, and the key psychometric properties assessed to evaluate their effectiveness in enhancing our understanding of emotional and personality characteristics. Table 7 Summary of computerized tools for emotion and personality assessment Author/Year Computerized tools proposed/used Description of tool The population of study e.g. students, university students, women, etc. Age of study participants Study sample size Methodology Type of mental health concern Psychometric properties of tool assessed Smirnov et al. (2021)[ 62 ] Titanis A tool for intelligent text analysis in social media English-speaking social media users 18–65 years 1000 Cross-sectional Depression, anxiety, and stress Validity and Reliability De et al. (2015)[ 63 ] Eigenface approach A facial expression recognition algorithm based on the eigenface approach Indian university students 18–25 years 100 Machine Learning Anxiety Accuracy and Sensitivity Exler et al. (2016)[ 64 ] Wearable system for mood assessment A system that uses smartphone features and data from mobile ECGs to assess mood German adults 22–46 years 6 Mobile App Data Analysis Stress Validity and User Experience Ovur et al. (2021)[ 65 ] Autonomous learning framework for sEMG-based hand gesture recognition A machine learning framework that uses surface electromyography (sEMG) and depth data to recognize hand gestures Healthy adults 22–30 years 10 Machine Learning and Gesture Analysis None Precision and Recall Azam et al. (2019)[ 66 ] Smartphone-based mindful breathing app A smartphone-based app that guides users through mindful breathing exercises University students 18–25 years 100 Randomized controlled trial Depression and anxiety Reliability and Sensitivity Nave et al. (2018)[ 67 ] Social media mobile photography Using social media mobile photography to self-track emotional states General Smartphone Users 18–25 years 30 Mobile App Data Collection Emotional Well-being Test-retest Reliability Villatoro-Tello et al. (2021)[ 68 ] Clinical interviews as a support tool for depression detection Speech analysis tool for emotion detection in clinical interviews Adults 18–65 years 100 Machine learning Mood Disorders Sensitivity and Specificity Cheong et al. (2020)[ 69 ] Wearable sensor-based system Physiological sensors for monitoring mood changes in elderly patients Elderly individuals 65 + years 20 Physiological Monitoring Mood Changes in the Elderly Sensitivity and Specificity Fadhil et al. (2019)[ 70 ] CoachAI A conversational agent assisted health coaching platform General Smartphone Users 18–25 years 20 Natural Language Processing (NLP) Emotional Well-being Validity and User Experience Metin et al. (2022)[ 71 ] Deep learning method A deep learning method that uses EEG data to differentiate patients with bipolar disorder from controls Patients with Bipolar Disorder 18–65 years 100 Electroencephalography (EEG) Bipolar Disorder Test-retest Reliability Cuijpers et al. (2016)[ 72 ] Internet-based cognitive behavioral therapy (iCBT) A self-guided, computer-based program that teaches CBT skills for depression Adults with depression 18–65 years old 242 Randomized controlled trial Depression Efficacy, acceptability, and usability Andrews et al. (2018)[ 73 ] Virtual reality exposure therapy (VRET) A computer-generated simulation that exposes people to feared situations in a safe and controlled environment Adults with anxiety disorders 18–65 years old 100 Randomized controlled trial Anxiety disorders Efficacy, acceptability, and usability Wolf et al. (2016)[ 74 ] Mobile phone intervention A mobile phone intervention that provides guided mindfulness exercises to people with depressive symptoms Adults with depression 18–65 years old 50 Cross-sectional Depression Efficacy, acceptability, and usability Overall, the results and findings provide a comprehensive synthesis of the current state of research on computer-based tools employing machine learning techniques for emotion and personality identification. This section highlights the strengths and limitations of the identified tools, guiding researchers and practitioners in developing more accurate and reliable means of understanding human emotions and personality traits. RQ1: What are the existing challenges of developing tools for identifying emotions and personality traits? Identifying emotions and personality traits using computer-based tools is a burgeoning field with numerous challenges. These challenges span technological, ethical, and psychological domains, reflecting the complexity of capturing and interpreting human emotions and personality traits through digital means. Below, we delve into the existing challenges faced in the development of such tools, supported by relevant references. Obtaining high-quality and diverse datasets for training machine learning models is a fundamental challenge. Biases in training data, limited sample sizes, and data privacy concerns can hinder the development of accurate emotion and personality detection tools. Emotions and personality are multidimensional constructs, and integrating insights from psychology, neuroscience, and computer science is challenging. Ensuring that tools accurately capture these complex aspects of human behavior is an ongoing struggle. Emotions and personality traits can vary across cultures, leading to the challenge of developing tools that are culturally sensitive and applicable to a global audience [ 75 ] [ 76 ]. Achieving real-time emotion detection and personality assessment remains a challenge, particularly in contexts where timely responses are critical, such as mental health applications [ 77 ] [ 78 ]. Gathering sensitive emotional and personality data raises significant ethical questions. Safeguarding user privacy, obtaining informed consent, and protecting against data breaches are essential but challenging aspects of tool development [ 79 ] [ 80 ]. Emotions are inherently subjective, making it difficult to create objective and universally applicable measurement tools. The challenge lies in developing methods that can reliably interpret subjective emotional experiences [ 81 ]. Integrating data from various sources, such as text, facial expressions, physiological signals, and voice, into a cohesive assessment of emotions and personality traits is a complex challenge requiring advanced multimodal fusion techniques [ 82 ]. Ensuring the validity and generalizability of tools across diverse populations and contexts is a continual challenge. Validation methodologies need to evolve to encompass the intricacies of emotions and personality [ 83 ]. The development of tools that users find acceptable and easy to use is vital for adoption. The challenge is in designing interfaces and interactions that are user-friendly and nonintrusive [ 84 ]. Machine learning models trained on biased data can perpetuate stereotypes and inequalities. Ensuring fairness in emotion and personality assessment tools is an ongoing challenge [ 85 ]. The development of tools for identifying emotions and personality traits is a multifaceted endeavor that involves technological, ethical, and psychological complexities. Addressing these challenges requires collaboration across disciplines, rigorous data collection and analysis, ethical considerations, and a commitment to ongoing improvement in tool design and validation. The following table summarizes the existing challenges of developing tools for identifying emotions and personality traits. Table 8 Challenges in Developing Tools for Identifying Emotions and Personality Traits Challenge Description References Obtaining high-quality and diverse datasets for training machine learning models Biases in training data, limited sample sizes, and data privacy concerns can hinder the development of accurate emotion and personality detection tools. [ 75 ], [ 76 ] Integrating insights from psychology, neuroscience, and computer science Emotions and personality traits are multidimensional constructs, and integrating insights from various disciplines is challenging. [ 75 ], [ 76 ] Cultural sensitivity and global applicability Emotions and personality traits can vary across cultures, leading to the challenge of developing tools that are culturally sensitive and applicable to a global audience. [ 75 ], [ 76 ] Real-time emotion detection and personality assessment Achieving real-time detection and assessment, particularly in critical contexts like mental health applications, remains challenging. [ 77 ], [ 78 ] Ethical considerations and data privacy Safeguarding user privacy, obtaining informed consent, and protecting against data breaches are essential but challenging aspects of tool development. [ 79 ], [ 80 ] Subjectivity of emotions Emotions are inherently subjective, making it difficult to create objective and universally applicable measurement tools. [ 81 ] Multimodal fusion techniques Integrating data from various sources into a cohesive assessment of emotions and personality traits requires advanced multimodal fusion techniques. [ 82 ] Validity and generalizability across diverse populations and contexts Ensuring the validity and generalizability of tools across diverse populations and contexts is a continual challenge. [ 83 ] User acceptance and usability Developing tools that users find acceptable and easy to use is vital for adoption. The challenge is in designing user-friendly and nonintrusive interfaces and interactions. [ 84 ] Fairness in machine learning models Machine learning models trained on biased data can perpetuate stereotypes and inequalities. Ensuring fairness in emotion and personality assessment tools is an ongoing challenge. 85] Table 8 provides a concise overview of the challenges encountered in the development of tools for identifying emotions and personality traits, along with relevant references supporting each challenge. RQ1.1 What are the data collection methods used in identifying emotions and personality traits? In studies aimed at identifying emotions and personality traits, various data collection methods are employed to gather the necessary information for analysis. These methods are chosen based on the research objectives, the nature of the traits being studied, and the available resources. There are several common data collection methods used in such studies. Surveys and questionnaires are widely used to collect self-reported data on personality traits and emotional experiences. Participants answered a series of standardized questions designed to assess their personality characteristics or emotional states. Examples include the Big Five Inventory for Personality Traits and the Positive and Negative Affect Schedule (PANAS) for emotions [ 26 ] [ 27 ]. Interviews, including structured, semistructured, and open-ended formats, allow researchers to gather in-depth information about an individual's emotions and personality. Clinical interviews, for instance, are used in psychological assessments to diagnose personality disorders [ 62 ] [ 68 ] [ 71 ]. Behavioral observations involve systematically watching and recording an individual's actions, facial expressions, body language, and verbal cues to infer their emotional states and personality traits. This method is often used in clinical and research settings. Physiological data collection methods, such as electrocardiography (ECG), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI), are used to monitor physiological responses associated with emotions [ 64 ] [ 65 ] [ 71 ] [ 72 ]. These measures can provide objective data on emotional reactions. Textual data sources, such as social media posts, emails, or written essays, can be analyzed using natural language processing (NLP) techniques to extract emotional content and personality traits. Sentiment analysis and linguistic analysis are common approaches. Facial expression analysis involves using computer vision techniques to recognize and analyze facial expressions in images or videos [ 71 ]. Facial expression analysis is a valuable method for assessing emotions noninvasively. Experimental tasks and games are designed to elicit specific emotional responses or behaviors from participants. These tasks are often used in psychology and neuroscience studies to study emotions in controlled settings. Wearable sensors, such as heart rate monitors and skin conductance sensors, can capture physiological changes associated with emotions in real time [ 69 ]. These sensors are used in both research and clinical applications. Mobile applications and smart devices with built-in sensors, such as accelerometers and GPSs, can collect data on users' behaviors, movements, and locations, which can be indicative of emotions and personality traits. Biometric data, including fingerprints, iris scans, and voice recordings, can be used to identify unique characteristics related to personality traits and emotional states [ 66 ] [ 67 ] [ 74 ]. The choice of data collection method depends on the specific research goals, the target population, and the feasibility of the approach. Many studies employ a combination of these methods to obtain a comprehensive understanding of emotions and personality traits. In Table 7 , we outline and discuss the diverse array of data collection methods commonly utilized in studies aimed at identifying emotions and personality traits. Each method offers unique insights and advantages, contributing to a comprehensive understanding of human behavior and psychological processes. Table 9 Data collection methods for identifying emotions and personality traits Data Collection Method Description Example Studies Surveys and Questionnaires Participants answer standardized questions to assess personality traits or emotional states. [ 26 ], [ 27 ] Interviews Structured or semistructured interviews gather in-depth information about emotions and personality. [ 62 ], [ 68 ], [ 71 ] Behavioral Observations Systematic observation of actions, facial expressions, and verbal cues to infer emotions and traits. [ 71 ] Physiological Data Collection Monitoring physiological responses such as ECG, EEG, and fMRI to assess emotions. [ 64 ], [ 65 ], [ 71 ], [ 72 ] Textual Data Analysis Analyzing social media posts or written text to extract emotional content and personality traits using NLP. [ 71 ] Facial Expression Analysis Using computer vision techniques to recognize and analyze facial expressions in images or video. [ 71 ] Experimental Tasks and Games Designed tasks to elicit specific emotional responses in controlled settings. [ 69 ] Wearable Sensors Using sensors like heart rate monitors to capture physiological changes associated with emotions. [ 69 ] Mobile Applications and Smart Devices Collecting behavioral data, movements, and locations from mobile apps and smart devices. [ 66 ], [ 67 ], [ 74 ] Biometric Data Utilizing biometric data like fingerprints and voice recordings to identify unique characteristics related to emotions and traits. [ 66 ], [ 67 ] The methods shown in Table 9 offer various ways to gather data on emotions and personality traits, providing researchers with diverse approaches to studying these phenomena. RQ1.2 What are the data analysis methods used in identifying emotions and personality traits studies? Identifying emotions and personality traits in studies involves the use of various data analysis methods to process and make sense of the collected data. The choice of analysis methods depends on the nature of the data, the research objectives, and the complexity of the traits being studied. There are several common data analysis methods used in such studies. Descriptive statistics, such as the mean, median, and standard deviation, are used to summarize and describe the central tendencies and distributions of personality trait scores or emotional responses obtained from surveys or questionnaires [ 26 ]. Factor analysis is employed to identify underlying factors or dimensions within a set of observed variables. In personality research, the Big Five personality traits (i.e., openness, conscientiousness, extraversion, agreeableness, neuroticism) are often extracted from a pool of related questionnaire items [ 27 ]. Cluster analysis groups individuals with similar personality profiles or emotional responses into clusters or categories. It can help individuals identify distinct personality types or emotional patterns within a population [ 62 ]. Regression analysis is used to explore relationships between personality traits or emotional states and other variables. For example, it can be used to examine how personality traits predict specific behaviors or outcomes [ 71 ]. Machine learning techniques, including classification and regression algorithms, are increasingly used to predict and classify emotions and personality traits based on various data sources. Support vector machines, decision trees, and neural networks are common choices [ 63 ] [ 65 ]. NLP methods are employed to analyze textual data, such as social media posts or written content, to extract emotional content, sentiment, or personality traits. Techniques include sentiment analysis and text classification [ 71 ]. Content analysis involves systematically coding and categorizing qualitative data, such as interview transcripts or open-ended survey responses, to identify themes and patterns related to emotions and personality [ 64 ] [ 65 ]. In studies that involve neuroimaging data (e.g., fMRI, EEG), sophisticated image analysis techniques are used to identify brain regions associated with specific emotions or personality traits [ 71 ] [ 72 ]. In facial expression analysis, machine vision algorithms are used to detect and classify facial expressions, helping to identify emotional states based on facial cues [ 63 ]. Time series analysis is applied when studying how emotions or personality traits change over time. It can reveal temporal patterns and trends in emotional responses [ 65 ]. SEM is used to test complex models that involve multiple variables and relationships. It can be used to examine how personality traits interact and influence outcomes [ 69 ]. Biometric data, such as heart rate variability or skin conductance, are analyzed to identify physiological patterns associated with specific emotional states. Data visualization techniques, including charts, graphs, and heatmaps, are used to present and interpret complex data related to emotions and personality traits [ 71 ]. The choice of data analysis methods depends on the research goals and the type of data collected. Many studies combine multiple methods to gain a comprehensive understanding of emotions and personality traits, especially when using diverse data sources such as surveys, physiological measures, and textual data. The following table summarizes the data analysis methods used in identifying emotions and personality trait studies: Table 10 Summary of Data Analysis Methods for Identifying Emotions and Personality Traits Data Analysis Method Description Descriptive Statistics Summarizes central tendencies and distributions of personality trait scores or emotional responses obtained from surveys or questionnaires. Factor Analysis Identifies underlying factors or dimensions within observed variables, often used to extract the Big Five personality traits from related questionnaire items. Cluster Analysis Groups individuals with similar personality profiles or emotional responses into clusters or categories, aiding in identifying distinct personality types. Regression Analysis Explores relationships between personality traits or emotional states and other variables, predicting specific behaviors or outcomes. Machine Learning Techniques Predicts and classifies emotions and personality traits based on various data sources, including support vector machines, decision trees, and neural networks. Natural Language Processing Analyzes textual data, such as social media posts or written content, extracting emotional content, sentiment, or personality traits using sentiment analysis. Content Analysis Systematically codes and categorizes qualitative data, such as interview transcripts or open-ended survey responses, identifying themes related to emotions. Neuroimaging Analysis Utilizes sophisticated image analysis techniques to identify brain regions associated with specific emotions or personality traits. Facial Expression Analysis Employs machine vision algorithms to detect and classify facial expressions, aiding in identifying emotional states based on facial cues. Time Series Analysis Examines how emotions or personality traits change over time, revealing temporal patterns and trends in emotional responses. Structural Equation Modeling Tests complex models involving multiple variables and relationships, examining how personality traits interact and influence outcomes. Biometric Data Analysis Analyzes physiological patterns associated with specific emotional states, including heart rate variability or skin conductance. Data Visualization Techniques Presents and interprets complex data related to emotions and personality traits using charts, graphs, and heatmaps. Table 10 provides an overview of the various data analysis methods employed in studies focused on identifying emotions and personality traits, highlighting the diverse approaches used to gain insights into human behavior and psychological processes. RQ2 - What are the key open issues in developing tools for identifying emotions and personality traits? Identifying emotions and personality traits through tools and technologies is an evolving field, and several key open issues persist. These challenges reflect the complexity of the human psyche and the dynamic nature of emotions and personality traits. There are several key open issues. Effective collaboration between psychologists, computer scientists, neuroscientists, and other experts is essential. Bridging the gap between these disciplines remains a challenge, as each discipline provides unique insights into the study of emotions and personality [ 1 ][ 2 ]. Existing tools may not accurately capture emotions and personality traits across diverse cultural contexts. The development of culturally sensitive assessment tools and the accounting for cultural variations remain open issues [ 3 ]. Real-time emotion detection and personality assessment are crucial for applications such as mental health support and human-computer interaction. The development of tools that can provide timely and accurate assessments in dynamic environments is a challenge [ 86 ]. The collection of personal data related to emotions and personality raises significant ethical questions. Balancing the need for data with privacy and ethical considerations remains an ongoing issue [ 87 ]. Emotions are inherently subjective and influenced by context [ 88 ]. The development of tools that can account for individual subjectivity and situational context is a complex challenge. Integrating data from various sources, such as text, facial expressions, physiological signals, and voice, into a coherent assessment of emotions and personality traits is an open issue. Advanced multimodal fusion techniques are needed [ 89 ]. Ensuring the validity and generalizability of assessment tools across diverse populations and contexts is an ongoing challenge [ 90 ]. Validation methodologies must evolve to encompass the intricacies of emotions and personality. Machine learning models trained on biased data can perpetuate stereotypes and inequalities [ 91 ]. Ensuring fairness in emotion and personality assessment tools is a critical issue that requires attention. The development of tools that users find acceptable and easy to use is vital for adoption. The challenge is in designing interfaces and interactions that are user-friendly and nonintrusive [ 92 ]. Understanding how emotions and personality traits change over time and in response to interventions or life events is a significant open issue. Longitudinal studies are needed to address this aspect. Creating tools that can provide personalized insights into emotions and personality traits for individuals is an emerging challenge. Personalization requires the integration of diverse data sources and adaptive algorithms. Ensuring that AI-based tools for emotion and personality assessment adhere to ethical principles, such as transparency, accountability, and fairness, is a pressing issue [ 93 ] [ 94 ]. Extending the use of emotion and personality assessment tools to domains beyond psychology, such as healthcare, education, and marketing, poses open challenges in adapting and validating these tools for new contexts. Combining the outputs of automated tools with human judgment and expertise is a complex issue [ 95 ]. The development of hybrid systems that leverage both automated and human assessments is an open area of research. Emotions are dynamic and can change rapidly. Modeling these dynamics and their impact on decision-making and behavior is a challenging research problem [ 96 ]. Addressing these open issues in the development of tools for identifying emotions and personality traits will require ongoing collaboration, multidisciplinary approaches, and innovative research across psychology, computer science, and related fields. The following table summarizes the key open issues in developing tools for identifying emotions and personality traits: Table 11 Key Open Issues in Developing Tools for Identifying Emotions and Personality Traits Open Issue Description Effective Collaboration between Disciplines Bridging the gap between psychologists, computer scientists, neuroscientists, and other experts to leverage diverse insights into emotions and personality. Cultural Sensitivity Developing assessment tools that accurately capture emotions and personality traits across diverse cultural contexts. Real-time Assessment Developing tools capable of providing timely and accurate assessments of emotions and personality traits in dynamic environments, such as mental health support and HCI applications. Ethical Considerations Balancing the collection of personal data related to emotions and personality with privacy and ethical considerations. Subjectivity and Context Developing tools that account for individual subjectivity and situational context in assessing emotions and personality traits. Multimodal Fusion Integrating data from various sources (e.g., text, facial expressions, physiological signals) into a coherent assessment of emotions and personality traits. Validity and Generalizability Ensuring the validity and generalizability of assessment tools across diverse populations and contexts. Fairness Addressing biases in machine learning models used for emotion and personality assessment to ensure fairness. User Acceptance Designing user-friendly interfaces and interactions for emotion and personality assessment tools. Longitudinal Studies Conducting longitudinal studies to understand how emotions and personality traits change over time and in response to interventions. Personalization Developing tools that provide personalized insights into emotions and personality traits for individuals. Ethical AI Ensuring AI-based tools adhere to ethical principles such as transparency, accountability, and fairness. Extending Applications Adapting and validating emotion and personality assessment tools for new contexts beyond psychology. Hybrid Systems Developing hybrid systems that combine automated and human assessment for more accurate results. Modeling Dynamics Modeling the dynamic nature of emotions and their impact on decision-making and behavior. Table 11 provides a structured overview of the key open issues, allowing for easy reference and understanding of the challenges in developing tools for identifying emotions and personality traits. RQ3-What types of contributions have been proposed in this research field? In the research field of identifying emotions and personality traits using computer-based tools, various types of contributions have been proposed. These contributions encompass a wide range of advancements, innovations, and applications. There are some common types of contributions made in this field. Researchers have proposed novel algorithms and machine learning models for accurately detecting emotions and assessing personality traits. These advancements have led to more robust and reliable tools for automated analysis [ 63 ]. Contributions include the development of effective feature extraction methods for different data sources, such as text, speech, facial expressions, and physiological signals. These methods improve the quality of the input data for analysis [ 62 ] [ 63 ]. Many contributions have focused on integrating data from multiple sources (e.g., text, audio, video) to provide a holistic understanding of emotions and personality traits. Cross-modal fusion techniques have been proposed for this purpose [ 8 ]. Researchers have created and shared large datasets containing emotional and personality data, enabling the development and validation of new tools. These databases have contributed to the advancement of the field [ 9 ]. The contributions of this study include rigorous validation studies that assess the accuracy and reliability of emotion and personality assessment tools. Validation ensures that the tools are suitable for various applications [ 6 ]. Research has been conducted to investigate the cross-cultural applicability of emotion and personality assessment tools. Understanding cultural variations is essential for creating universally valid tools [ 7 ]. Contributions have been made in applying emotion and personality assessment tools to healthcare contexts. These tools help in diagnosing and monitoring mental health conditions and providing personalized interventions [ 10 ]. In the field of HCIs, contributions involve the development of user interfaces and systems that adapt based on users' emotional states and personality traits, enhancing user experiences [ 11 ]. Researchers have proposed personalized approaches to emotion and personality assessment, tailoring recommendations and interventions based on individual profiles [ 97 ]. Ethical contributions address the responsible use of tools for emotion and personality assessment. Ethical guidelines and frameworks help ensure user privacy and data security [ 98 ]. Contributions extend to commercial applications, where emotion and personality assessment tools are integrated into marketing, customer service, and product design to enhance user engagement and satisfaction [ 99 ]. Tools for identifying emotions and personality traits are applied in education and training settings to tailor instructional content and support personalized learning experiences [ 71 ]. This field advances psychological research by providing new methods and tools for studying emotions and personality traits in controlled and real-world settings. Researchers have proposed predictive models that use emotion and personality data to forecast behaviors, such as consumer choices, social interactions, and mental health outcomes [ 72 ]. Contributions include fostering collaboration between psychologists, computer scientists, neuroscientists, and other experts, leading to a more holistic understanding of emotions and personality. The development of open-source software and libraries for emotion and personality analysis allows for broader access and collaboration within the research community [ 69 ] [ 73 ]. These types of contributions collectively contribute to the advancement of the field of identifying emotions and personality traits, enabling its application in various domains and addressing complex challenges. The ongoing collaboration between researchers and practitioners continues to drive innovation in this interdisciplinary field. Table 12 Types of Contributions in the Research Field of Identifying Emotions and Personality Traits Type of Contribution Description Reference Novel Algorithms Researchers propose innovative algorithms and models for emotion and personality assessment, enhancing automated analysis.. [ 86 ] Data Integration Efforts focus on integrating data from various sources to provide a holistic understanding of emotions and personality traits, contributing to comprehensive analysis. [ 87 ] Validation Studies Contributions include rigorous validation studies to assess the accuracy and reliability of emotion and personality assessment tools, enhancing their credibility. [ 88 ] Cross-Cultural Research Research investigates the cross-cultural applicability of emotion and personality assessment tools, aiming to ensure universal validity and inclusivity. [ 89 ] Healthcare Applications Contributions involve the application of emotion and personality assessment tools in healthcare settings, enhancing patient care and mental health treatment. [ 90 ] HCI Enhancements Research focuses on developing user interfaces and systems that adapt based on users' emotional states and personality traits, improving user experiences. [ 91 ] Personalization Researchers propose personalized approaches to emotion and personality assessment, tailoring interventions based on individual profiles. [ 92 ] Ethical Considerations Contributions address ethical considerations in emotion and personality assessment, promoting responsible usage and data security. [ 93 ] Commercial Applications Tools for identifying emotions and personality traits are integrated into commercial applications to enhance user engagement and satisfaction. [ 94 ] Education Applications Contributions extend to education settings, where tools are used to tailor instructional content and support personalized learning experiences. [ 95 ] Table 12 summarizes the various types of contributions made in the research field of identifying emotions and personality traits using computer-based tools. RQ4- What is the most focal topic of the publication trend in the studies identifying emotions and personality traits during the last five years from the selected studies? The most focal topic of the publication trend in studies identifying emotions and personality traits during the last five years has been the use of machine learning and artificial intelligence (AI) [ 63 ] [ 64 ] [ 65 ] [ 67 ] [ 68 ]. This is evidenced by the rapid increase in the number of publications using these methods, as well as the increasing sophistication of the models being developed. One of the main advantages of using machine learning and AI for emotion and personality recognition is that they can be used to analyze large amounts of data quickly and accurately. This is important because emotions and personality traits can be difficult to identify manually, especially in real time. Another advantage of using machine learning and AI is that they can be used to analyze a wide variety of data types, including text, speech, images, and videos. This allows researchers to develop more comprehensive and accurate models of emotion and personality recognition. Some specific examples of the use Table 13 Publication trends in studies identifying emotions and personality traits Most Focal Topic Description Reference Use of Machine Learning and AI Dominant trend in recent research on identifying emotions and personality traits, characterized by the increasing use of machine learning and artificial intelligence (AI) methods. [ 96 ] Advantages of Machine Learning and AI Highlights the benefits of using machine learning and AI for emotion and personality recognition, including the ability to analyze large datasets quickly and accurately, and the versatility in handling various data types. [ 97 ] Specific Examples of Machine Learning and AI Use Provides examples of machine learning applications in identifying emotions and personality traits, such as facial expression analysis, text and speech analysis, and image and video analysis, with diverse applications across different fields. [ 98 ] Potential Applications Discusses the potential applications of machine learning and AI in emotion and personality identification, including human-computer interaction, customer service, mental health diagnosis, and crime prevention, indicating the wide-ranging impact of these technologies. [ 99 ] of machine learning and AI for emotion and personality recognition include the following: Machine learning can be used to identify facial expressions, which are key indicators of emotion. For example, a machine learning model could be trained to identify the facial expressions associated with happiness, sadness, anger, and fear [ 18 ] [ 19 ] [ 23 ]. Machine learning can be used to analyze text for emotional cues, such as the use of certain words or phrases. For example, a machine learning model could be trained to identify tweets that express happiness, sadness, anger, or fear [ 16 ] [ 17 ] [ 22 ]. Machine learning can be used to analyze speech for emotional cues, such as the tone of voice, pitch, and rhythm of speech. For example, a machine learning model could be trained to identify phone calls where the caller is expressing anger or distress [ 20 ] [ 21 ] [ 24 ]. Machine learning can be used to analyze images for emotional cues, such as the expressions of people's faces or the body language of people in a scene. For example, a machine learning model could be trained to identify images that depict happiness, sadness, anger, or fear [ 18 ] [ 19 ] [ 23 ]. Machine learning can be used to analyze videos for emotional cues, such as facial expressions, body language, and tone of voice. For example, a machine learning model could be used to identify videos where people express happiness, sadness, anger, or fear [ 18 ] [ 19 ] [ 25 ]. The use of machine learning and AI for emotion and personality recognition has a wide range of potential applications. For example, it could be used to develop more engaging and personalized human-computer interaction systems, to improve customer service, to develop new diagnostic tools for mental health disorders, and to develop new ways to detect and prevent crime. Overall, the publication trend in studies identifying emotions and personality traits during the last five years has been marked by the increasing use of machine learning and AI, as well as the development of new and improved methods for data collection, labeling, and ethical considerations. These findings provide insights into the increasing prominence of machine learning and AI in the study of identifying emotions and personality traits during the last five years, as well as the wide range of potential applications and benefits associated with these technologies. A. CHANNEL OF PUBLICATION The "channel of publication" refers to the specific venue or platform through which research findings, papers, articles, and other scholarly works are made publicly available to the academic community and the broader public. The choice of publication channel can significantly impact the visibility, accessibility, and credibility of research. Here, we will discuss the concept of the publication channel in more detail: Publishing the research findings in reputable academic journals is a common and respected channel for disseminating research in various fields. In this case, the study could be submitted to journals that focus on topics related to artificial intelligence, machine learning, affective computing, psychology, human-computer interaction, or any field closely related to the research. These journals typically require rigorous peer review, ensuring the quality and credibility of the research. Presenting the research at conferences and subsequently publishing the proceedings is common in many academic and technical fields. Depending on the subject matter, conferences related to artificial intelligence, machine learning, emotion analysis, and personality assessment may be suitable. Conference papers offer a platform for presenting research to a specialized audience and can facilitate discussions and feedback from experts in the field. Figure 7 shows the number of systematic mapping studies published from 2019 to 2023. In-depth studies or comprehensive reviews are often published as books or book chapters. The research could be expanded into a book or contribute a chapter to an edited volume related to the study's focus. This allows for a more extensive exploration of the topic and provides a reference for scholars and practitioners in the field. If the research is part of a doctoral or master's thesis, it can be made accessible through the university's library and online repository. This channel is particularly relevant if the research is conducted within an academic institution. The choice of publication channel should align with the research's objectives, target audience, and the level of detail and rigor needed. Researchers may also consider multiple channels to reach different audiences and maximize the impact of their findings. B. PUBLICATION TREND The publication trend for systematic mapping studies of tools to identify emotions and personality traits from 2019 to 2023 shows a steady increase in the number of publications each year. This trend is likely due to a number of factors. The availability of data on emotions and personality traits is increasing. These data are collected from a variety of sources, including social media, wearable devices, and surveys. The increasing sophistication of machine learning and AI algorithms. These algorithms can be used to analyze large amounts of data to identify patterns and trends that are difficult or impossible to identify manually. There is growing interest in the use of emotion and personality recognition in a variety of applications, such as human-computer interaction, customer service, mental health, and security. Figure 8 shows the number of systematic mapping studies on tools for identifying emotions and personality traits published each year from 2019 to 2023. As shown in Fig. 8 , the number of publications increased from 25 in 2019 to 60 in 2023. This represents a fivefold increase over the five-year period. The following are some of the key findings from the systematic mapping studies published during this period. The majority of the tools surveyed use machine learning and AI algorithms to identify emotions and personality traits [ 63 ] [ 64 ] [ 65 ] [ 67 ] [ 68 ]. The most common data types used to train machine learning models are text, speech, and images [ 16 ] [ 17 ] [ 18 ] [ 19 ] [ 20 ]. The most common applications of these tools are human-computer interaction, customer service, mental health, and security. The publication trend for systematic mapping studies on tools to identify emotions and personality traits suggests that this is a rapidly growing field with a wide range of potential applications. IX. DISCUSSION By comparing and contrasting the different data sources utilized for emotion and personality identification, our study delves into the unique methodologies and machine learning techniques employed across various categories, including text analysis, facial expression recognition, gesture analysis, and physiological signal monitoring [ 16 ] [ 18 ] [ 20 ] [ 23 ]. Through this analysis, we elucidate the strengths and limitations of each approach, considering factors such as accuracy, reliability, and scalability. Furthermore, we thoroughly examined the validity and reliability of the identified tools, critically assessing the appropriateness of the validation methods and potential sources of bias inherent in each technique. Furthermore, this study explored the practical applications and use cases of these tools in diverse domains, such as marketing, healthcare, and personalized systems. By highlighting real-world scenarios where these tools can offer valuable insights and support decision-making processes, we underscore their potential impact on improving individual well-being and enhancing user experiences [ 56 ] [ 57 ]. Additionally, we address ethical considerations and privacy concerns associated with the use of sensitive data for emotion and personality identification, emphasizing the importance of informed consent and responsible data handling practices. Moreover, our study delves into the challenges and opportunities for future research in this domain. We advocate for a focus on cross-cultural validation, emphasizing the need for tools that are sensitive to cultural nuances and applicable across diverse populations [ 56 ]. Additionally, we highlight the importance of real-time emotion and personality recognition, envisioning the development of systems capable of dynamically adapting to users' changing emotional states and personality traits. Furthermore, we advocate for multimodal approaches that integrate various data sources, recognizing the potential for synergy and improved accuracy in combining textual, visual, and physiological signals [ 57 ]. Finally, this study underscores the significance of computer-based tools in advancing our understanding of human emotions and personality traits. However, we emphasize the need for robust validation methods and ethical considerations to ensure the reliability and credibility of these applications. By addressing these challenges and embracing future research directions, we anticipate that advancements in this field will contribute to more accurate and reliable tools for understanding and supporting human emotions and personality traits in diverse contexts. X. CONCLUSION In conclusion, this systematic mapping study provides a comprehensive overview of the current state of research on computer-based tools employing machine learning techniques for emotion and personality identification. The findings highlight the significance of emotions and personality traits in shaping human behavior, cognition, and overall well-being. The research landscape reveals a diverse range of data sources, including text analysis, facial expression recognition, gestures, and physiological signals, harnessed by sophisticated machine learning algorithms to gain deeper insights into an individual's psychological makeup. The results underscore the effectiveness of natural language processing techniques in capturing emotions from textual data, while advanced deep learning models demonstrate remarkable accuracy in facial expression recognition. Moreover, gesture recognition and physiological signal analysis using machine learning algorithms offer promising avenues for understanding personality traits and emotional responses. However, the discussion also highlights the importance of standardized validation procedures and considerations of potential biases to ensure the credibility and reliability of the identified tools. The systematic mapping study presented here provides valuable insights for researchers, practitioners, and developers interested in leveraging machine learning to gain a deeper understanding of human emotions and personality characteristics. By considering the strengths, limitations, and ethical considerations of these tools, we can ensure responsible and impactful applications that benefit individuals and society as a whole. As technology continues to evolve, the development of empathetic and personalized systems can lead to enhanced user experiences, improved mental health interventions, and a deeper understanding of what it means to be human. Declarations Acknowledgments This project was supported by the Postgraduate Research Grants (PPP)-PG169-2024A and PG005-2024B from the University of Malaya, Malaysia. Declaration of Generative AI and AI-assisted technologies in the writing process During the preparation of this work , we used GPT-3.5, Google Bard, Quillbot and Grammarly to improve the quality of writing. After using this tool/service, we reviewed and edited the content as needed and take full responsibility for the content of the publication. References Abdullah, S. M. S. A., Ameen, S. Y. A., Sadeeq, M. 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16:45:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42937,"visible":true,"origin":"","legend":"\u003cp\u003eThe five types represent an individual’s personality [28].\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4356776/v1/52183b200e3ef1d1fa8c5d27.png"},{"id":56024772,"identity":"814a4c9c-3822-434d-8d3f-3a4ee36d124d","added_by":"auto","created_at":"2024-05-07 16:53:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21410,"visible":true,"origin":"","legend":"\u003cp\u003eThe Dark Triad of Personalities [29]\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4356776/v1/c9fd8a5694564be21946ed23.png"},{"id":56024769,"identity":"24ac89d0-da39-4c79-b671-d59cfeb43fa7","added_by":"auto","created_at":"2024-05-07 16:53:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26615,"visible":true,"origin":"","legend":"\u003cp\u003eThe 2D emotion model [30].\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4356776/v1/417844e08b225f8b53c0407a.png"},{"id":56024045,"identity":"57c1f438-617e-4d36-bb46-a71e51e54483","added_by":"auto","created_at":"2024-05-07 16:45:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61301,"visible":true,"origin":"","legend":"\u003cp\u003eSystematic Mapping Process [35]\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4356776/v1/76d5490e95fe58cc516c4590.png"},{"id":56024043,"identity":"20941d65-e6de-48f2-8e1b-f3956fddd617","added_by":"auto","created_at":"2024-05-07 16:45:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":28608,"visible":true,"origin":"","legend":"\u003cp\u003eClassification process[37]\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4356776/v1/ea8bc8bb11390b147b1dacfc.png"},{"id":56024042,"identity":"965573c5-d43e-42a4-88c7-3860ff52d40a","added_by":"auto","created_at":"2024-05-07 16:45:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":252578,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomy of tools for emotion and personality identification\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4356776/v1/4baf4cc058756f9519a26414.png"},{"id":56024039,"identity":"be1dfb4a-7571-4d16-8b21-7d9193cc7970","added_by":"auto","created_at":"2024-05-07 16:45:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":10755,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of systematic mapping studies published from 2019 to 2023\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4356776/v1/f419e889915fdf0c17646cb0.png"},{"id":56024041,"identity":"fc70fbff-8074-4b26-bd4a-a82bf4f893ae","added_by":"auto","created_at":"2024-05-07 16:45:30","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":7659,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of systematic mapping studies published from 2019 to 2023\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4356776/v1/fee6556a9c2c0dbfbaea5a0d.png"},{"id":56401478,"identity":"74f9ab0a-feca-4c50-8bda-0d72cdb1edfa","added_by":"auto","created_at":"2024-05-13 16:44:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2104661,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4356776/v1/70b6892e-c3d3-49de-9107-5c93c64112b5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSystematic Mapping Study of Tools to Identify Emotions and Personality Traits\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"I. INTRODUCTION","content":"\n\u003cp\u003eEmotions and personality traits are fundamental components of human behavior and play a significant role in shaping individual experiences, social interactions, and overall well-being. Understanding and accurately identifying emotions and personality traits can provide valuable insights for various fields, including psychology, human-computer interaction, healthcare, marketing, and personalized services [1]. In recent years, rapid advancements in machine learning and artificial intelligence have led to the development of sophisticated computer-based tools capable of recognizing and characterizing emotions and personality traits with increasing accuracy and efficiency [2]. These tools harness diverse data sources, such as text analysis, face recognition, gestures, and physiological signals such as heart rate monitoring, to gain a comprehensive understanding of an individual's psychological makeup [3].\u003c/p\u003e\n\n\u003cp\u003eResearch has demonstrated the potential of natural language processing (NLP) techniques for capturing emotions from textual data, such as social media posts, online reviews, and written communication [4]. These methods utilize sentiment analysis, emotion classification, and affective computing algorithms to discern the emotional tone and underlying sentiments expressed in texts [5]. Additionally, facial expressions are vital nonverbal cues that provide valuable insights into emotions [6]. Advanced deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown impressive capabilities in accurately recognizing and classifying facial expressions, paving the way for applications in areas such as human-computer interaction, virtual reality, and emotion-aware systems [7]. Moreover, gesture recognition and physiological signals, such as heart rate, have been explored as potential indicators of personality traits and emotional responses [8]. Machine learning algorithms, such as support vector machines (SVMs), decision trees, and neural networks, have been employed to analyze patterns in gestures and physiological signals to infer an individual's personality traits and emotional states [9]. These developments have significant implications for understanding human behavior and enhancing personalized services and interactions in various domains [10].\u003c/p\u003e\n\u003cp\u003eDespite the rapid growth in this area, the field of computer-based emotion and personality identification is diverse and fragmented. A systematic mapping study is essential for gathering, categorizing, and synthesizing the literature to identify trends, highlight potential challenges, and guide future research directions. By conducting a systematic mapping study, we aim to contribute to the consolidation of knowledge and inform researchers, practitioners, and developers working on emotion and personality recognition systems [11]. This systematic mapping study will provide an overview of the current state of research on computer-based tools employing machine learning to identify emotions and personality traits. We anticipate that the findings will facilitate advancements in understanding human psychology, enabling the development of more accurate and effective tools for a range of applications in psychology, technology, healthcare, marketing, and beyond.\u003c/p\u003e\n\u003cp\u003eThe objective of this systematic mapping study is to conduct a comprehensive and contemporary review of the research landscape surrounding computer-based tools employing machine learning techniques for the identification of emotions and personality traits. Through an extensive literature review encompassing peer-reviewed journal papers, this study seeks to identify cutting-edge methods and trends within this rapidly evolving domain. Specifically, our aim is to elucidate the potential of natural language processing (NLP) techniques for capturing emotions from textual data, the advancements in facial expression recognition enabled by deep learning models, and the exploration of gesture recognition and physiological signals as indicators of personality traits and emotional responses.\u003c/p\u003e\n\u003cp\u003eThe primary objective of our study was to explore the application of machine learning techniques in the identification of emotions and personality traits. However, the initial presentation of our research questions (RQs) may have obscured the specific objectives of the study. To rectify this, we will explicitly state our objectives as follows:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eTo investigate the effectiveness of machine learning models in identifying emotions and personality traits compared to traditional methods.\u003c/li\u003e\n\u003cli\u003eTo assess the scope and limitations of existing reviews in this area and identify research gaps that warrant further investigation.\u003c/li\u003e\n\u003cli\u003eTo provide insights into the specific reasons for conducting this study, there is a need to bridge existing knowledge gaps and contribute to advancements in the field of emotion and personality trait identification.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"II. BACKGROUND","content":"\u003cp\u003eThe field of emotion and personality research has a long history in psychology, with researchers exploring various methods to assess and understand these psychological constructs. Traditionally, psychologists have relied on self-report questionnaires, interviews, and observations to gauge emotions and personality traits. While these methods have been valuable, they often suffer from subjective biases and limitations in capturing real-time emotional states and subtle personality nuances. In recent years, the emergence of machine learning and artificial intelligence has brought about transformative changes in the field of emotion and personality identification. Researchers have increasingly turned to computer-based tools that leverage machine learning techniques to analyze vast amounts of data and make accurate inferences about an individual's emotions and personality traits.\u003c/p\u003e \u003cp\u003eIn the realm of natural language processing (NLP), sentiment analysis has gained substantial attention as a means to identify emotions from textual data. Researchers have developed sophisticated algorithms that can classify text as positive, negative, or neutral and, in some cases, even identify specific emotions such as joy, anger, sadness, or fear from written content [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These advancements have found practical applications in sentiment analysis for customer feedback, social media monitoring, and analyzing user interactions on various platforms. Facial expression recognition has emerged as another crucial area in computer-based emotion identification. Deep learning models, particularly convolutional neural networks (CNNs), have proven highly effective in accurately recognizing and categorizing facial expressions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These models can distinguish between various emotional states displayed in facial expressions, such as happiness, surprise, disgust, and fear. Facial expression recognition has practical implications in fields such as human-computer interaction, virtual reality, and affective computing.\u003c/p\u003e \u003cp\u003eMoreover, machine learning algorithms have been explored for analyzing gestures and physiological signals to infer personality traits and emotional states. Gesture recognition systems, powered by machine learning techniques, can capture and interpret body movements to infer an individual's personality traits, such as extraversion, openness, and agreeableness [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, machine learning has been applied to analyze physiological signals, such as heart rate variability, to detect and interpret emotional responses [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These developments open up new avenues for understanding emotions and personality traits through nonverbal cues and physiological responses. Despite these advancements, the field of computer-based emotion and personality identification remains diverse and fragmented. Studies often focus on specific data sources or limited sample sizes, which may restrict the generalizability of the findings. Additionally, the validation methods and standardization of machine learning models require further attention to ensure the reliability and effectiveness of the identified tools.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLiterature Review Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMachine Learning Techniques\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFindings and Applications\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShaik et al.(2023)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eText\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSentiment Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSentiment classification in online reviews\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLek et al.(2023)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eText\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmotion Classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFine-grained emotion labeling in text\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTripathi et al. (2021)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImages (Faces)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConvolutional Neural Networks(CNN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccurate facial expression recognition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXu et al. (2023)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImages (Faces)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeep Neural Networks (DNN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubtle emotion recognition using facial cues\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaijayanthi et al. (2022)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGestures, Body Posture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRandom Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGesture recognition for emotion identification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBota et al.(2019)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysiological Signals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupport Vector Machines (SVM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrelation of heart rate variability with emotions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang et al.(2020)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eText, Physiological Signals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLong Short-Term Memory (LSTM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCombined text and physiology for emotion recognition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMustafa et al. (2023)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImages (Faces)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ek-Nearest Neighbors (k-NN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-cultural facial expression recognition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomeo et al. (2019)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysiological Signals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecision Trees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAffective computing using physiological signals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWei et al. (2020)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGestures, Speech\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecurrent Neural Networks (RNN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultimodal fusion for emotion recognition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis systematic mapping study seeks to consolidate the existing knowledge and identify trends in the field of computer-based tools for emotion and personality identification using machine learning techniques.\u003c/p\u003e \u003cp\u003eBy reviewing and categorizing a wide range of research articles, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, this study aims to provide insights that will facilitate advancements in understanding human emotions and personality traits for various practical applications.\u003c/p\u003e \u003cp\u003eThe scope of our research encompasses the exploration of machine learning (ML) techniques in the identification of emotions and personality traits. Specifically, we aim to investigate the application of ML models in analyzing various data sources, including text, speech, facial expressions, physiological signals, and behavioral patterns, to infer emotional states and personality characteristics.\u003c/p\u003e \u003cp\u003eWithin this scope, our study focuses on the following:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAlgorithm Performance\u003c/b\u003e: We assess the effectiveness and accuracy of ML algorithms, such as neural networks, support vector machines, decision trees, and ensemble methods, in identifying emotions and personality traits. The evaluation metrics included the accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eData Requirements\u003c/b\u003e: We will examine the types and quantities of data required for training ML models for emotion and personality trait identification. This includes exploring the role of feature selection, data preprocessing techniques, and data augmentation strategies in enhancing model performance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEthical considerations\u003c/b\u003e: We will discuss the ethical implications of using ML techniques in this context, including issues related to data privacy, bias and fairness, interpretability of results, and potential societal impacts. We will also explore approaches for mitigating ethical concerns and promoting the responsible use of ML-based identification methods.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eComparison with Traditional Methods\u003c/b\u003e: We will compare ML-based identification approaches with traditional methods, such as manual coding, questionnaire-based assessments, and clinical interviews, to evaluate their relative strengths, limitations, and practical implications. This comparison will provide insights into the advantages and challenges of adopting ML techniques in this domain.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eApplication Domains\u003c/b\u003e: We explore the diverse application domains of ML-based emotion and personality trait identification, including mental health diagnosis and monitoring, human-computer interaction, personalized recommendations, marketing and advertising, and educational interventions. By examining these application areas, we aim to highlight the potential impact of ML-driven approaches in real-world settings.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eOur research seeks to contribute to a deeper understanding of the capabilities, challenges, and ethical considerations associated with using ML techniques for emotion and personality trait identification. By delineating the scope of our investigation, we aim to provide a comprehensive analysis that advances the state of the art in this rapidly evolving field.\u003c/p\u003e"},{"header":"III. EMOTION AND PERSONALITY TRAITS IN COMPUTER SCIENCE","content":"\u003cp\u003eIn the realm of computer science, the study of emotion and personality traits has gained significant importance. These psychological constructs offer valuable insights and applications, influencing fields such as human-computer interaction, artificial intelligence, sentiment analysis, and personalized computing. Two prominent aspects explored within computer science are the Big Five Personality Traits and the Dark Triad Personality Traits [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eBig Five Personality Traits\u003c/strong\u003e, also known as the Five-Factor Model, represent a widely accepted framework for characterizing human personality, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Within computer science, these traits are leveraged to enhance user experiences and tailor digital interactions [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. These five dimensions encompass the following:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eOpenness to Experience\u003c/strong\u003e: This trait relates to individuals who are open to novelty and creativity. In computer science, it can influence the design of innovative and user-friendly interfaces.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eConscientiousness\u003c/strong\u003e: Conscientious individuals are organized and goal-oriented. In computer science, this trait informs the development of efficient software systems and user workflows.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eExtraversion\u003c/strong\u003e: Extraverted individuals are outgoing and sociable. In computer science, this trait is considered in the creation of social and collaborative platforms.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAgreeableness\u003c/strong\u003e: Agreeable individuals are cooperative and considerate. In computer science, this trait influences the design of user interfaces that promote positive interactions.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eNeuroticism\u003c/strong\u003e: Neuroticism relates to emotional instability and stress susceptibility. In computer science, understanding this trait aids in developing stress-reduction applications and assessing user emotional states during interactions.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese personality traits play a vital role in creating personalized systems and adaptive technologies that cater to individual preferences and emotional states.\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eDark Triad Personality Traits\u003c/strong\u003e represent a different facet of personality characterized by socially undesirable traits, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Computer science has examined these traits for various purposes, including cybersecurity and fraud detection [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. The Dark Triad comprises the following:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMachiavellianism\u003c/strong\u003e: Individuals with high Machiavellianism tend to be manipulative and strategic. In computer security, understanding Machiavellian tendencies helps identify deceptive behavior and potential threats.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eNarcissism\u003c/strong\u003e: Narcissistic individuals exhibit an exaggerated sense of self-importance. Recognizing narcissistic behavior in the digital realm is crucial for addressing online harassment and cyberbullying issues.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003ePsychopathy\u003c/strong\u003e: Psychopathy encompasses traits such as callousness and a lack of empathy. In computer science, identifying psychopathic traits is relevant for cybersecurity and risk assessment, as psychopaths may engage in malicious online activities.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhile the Dark Triad traits are typically associated with negative behaviors, their study within computer science contributes to enhancing digital security and safeguarding against online threats.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmotions\u003c/strong\u003e are a fundamental aspect of human experience, and understanding them is becoming increasingly vital in the realm of computer science. Emotion-aware computing seeks to create systems that can not only recognize but also respond to human emotions effectively. By incorporating emotional intelligence into software and applications, computer scientists can significantly enhance user experiences. For instance, sentiment analysis algorithms can gauge user emotions in social media, customer reviews, and feedback, providing valuable insights for businesses and marketers [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. Emotion recognition in facial expressions, speech, and text allows for more empathetic chatbots and virtual assistants. Moreover, in healthcare applications, emotion-aware systems can contribute to stress management, mental health assessment, and patient care. As technology continues to advance, the ability to understand and respond to human emotions is poised to revolutionize how we interact with and benefit from computational systems [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eEmotions and personality traits play multifaceted roles within computer science. By optimizing user experiences based on the Big Five Personality Traits to bolster cybersecurity through an understanding of the Dark Triad, these constructs shape technology and human-computer interactions [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. Recognizing and integrating these insights will empower computer scientists to create more personalized, secure, and effective digital systems. Emotions, in particular, add a layer of complexity and depth to user interactions, making it increasingly important for computer scientists to incorporate emotional intelligence into their designs and applications [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows a schematic of the 2D emotion model. The 2D emotion model will be used to determine the target/real/actual emotion-related data labels as the ground truth to evaluate the emotion classification accuracy.\u003c/p\u003e"},{"header":"IV. METHODOLOGY","content":"\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cdiv\u003e\u003cstrong\u003eA. SYSTEMATIC MAPPING PEOCESS\u003c/strong\u003e\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe systematic mapping process serves as the backbone of this research, allowing for the comprehensive evaluation and organization of literature related to tools aimed at identifying emotions and personality traits [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. In line with the distinctive characteristics of systematic mapping studies, the process encompasses distinct stages that culminate in the creation of a systematic map that encapsulates the essence of the research domain [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the sequential stages of the systematic mapping process. It commences with the formulation of research questions, which serve as guiding beacons for the subsequent steps. This process entails a meticulous search for pertinent research papers across reputable databases. Next, the papers were subjected to rigorous screening based on predefined inclusion and exclusion criteria. The abstracts are methodically analyzed, keywords are identified, and data extraction is executed to distill key information from each paper. This extracted information is then mapped, leading to the creation of a systematic map that encapsulates the overarching trends and findings observed across the literature.\u003c/p\u003e\n\u003cp\u003eIn the domain of software engineering, systematic mapping studies have become prevalent, with Petersen\u0026apos;s guidelines serving as a cornerstone for conducting systematic mapping reviews [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. This methodology\u0026apos;s applicability extends beyond software engineering, permeating other research domains, including those that delve into emotion and personality trait identification. Notably, the fusion of Petersen\u0026apos;s systematic mapping study guidelines with Kitchenham\u0026apos;s systematic literature review guidelines has emerged as a best practice for conducting thorough systematic mapping studies [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. This composite approach combines the strengths of both methodologies, facilitating a nuanced and comprehensive exploration of the research landscape.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Kitchenham\u0026apos;s systematic mapping study [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] comprises five pivotal phases. The formulation of the research question marks its inception, serving as the cornerstone for subsequent exploration. The delineation of a robust search strategy leads to the identification of relevant papers. Subsequent phases encompass paper screening to refine the selection and a classification scheme phase, which aids in categorizing the selected papers. The culmination of the process involves mapping these studies onto a classification scheme, often visualized through a bubble plot graph. This systematic mapping technique, depicted in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, serves as the cornerstone of this research\u0026apos;s methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. RESEARCH QUESTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResearch questions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRQ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResearch Questions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMotivation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhat are the existing challenges of developing tools for identifying emotions and personality traits?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThis question aims to uncover the inherent challenges researchers face when creating tools to effectively identify emotions and personality traits. It sheds light on methodologies and data analysis techniques.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhat are the key open issues in developing tools for identifying emotions and personality traits?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThis question seeks to determine whether research designs, contexts, and objectives primarily revolve around exploring the identification of emotions and personality traits.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhat types of contributions have been proposed in this research field?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe objective of this question is to identify the diverse range of contributions made within the realm of emotion and personality trait identification, offering insights into proposed solutions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhat is the most focal topic of the publication trend in the studies of identifying emotions and personality traits from the selected studies during the last five years?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnveiling the central topics of recent publications within the past five years allows for the identification of influential trends, journals, conferences, and geographical distributions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe research undertaken revolves around conducting a systematic mapping study aimed at comprehensively exploring the landscape of tools designed for the identification of emotions and personality traits. The systematic mapping process serves as the foundational framework, systematically guiding the research through distinct phases. Beginning with the formulation of research questions, the study embarks on an extensive search for relevant research papers across reputable databases. These papers were meticulously screened based on predefined inclusion and exclusion criteria, and abstracts were meticulously analyzed to identify pertinent keywords and concepts. Subsequently, data extraction is performed to glean essential information from each paper. This culminates in the creation of a systematic map, visually encapsulating overarching trends and findings discerned from the literature. The research questions shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026mdash;ranging from elucidating challenges in tool development to identifying key contributions and publication trends\u0026mdash;guide this exploration. The study not only is anchored in Petersen\u0026apos;s systematic mapping study guidelines but also integrates Kitchenham\u0026apos;s systematic literature review guidelines, ensuring a comprehensive and robust methodology [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. This research aims to provide an incisive depiction of the dynamic field of tools for identifying emotions and personality traits, offering valuable insights to researchers, practitioners, and stakeholders in the interdisciplinary domains of psychology, human-computer interaction, healthcare, and more [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. CONDUCT A SEARCH FOR PRIMARY STUDIES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected specific databases, namely, the Web of Science (WoS), ScienceDirect, IEEEXplore, and ACM Digital Library, for our literature search on emotions and personality traits, as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The Springerlink database was excluded from our search due to limited access to full electronic articles. Additionally, we encountered restrictions in accessing only a few articles on Springerlink, with only titles and abstracts available. Instead, we found that the Web of Science (WoS) database provided access to a significant portion of important and relevant publications from Springerlink. We opted not to include Google Scholar in our search because it often yields results with overlap and lower precision compared to the selected databases. Moreover, Google Scholar lacks an \u0026quot;advanced search\u0026quot; option, whereas the four electronic databases we utilized encompass the majority of relevant \u0026quot;high-impact\u0026quot; journals present in Google Scholar.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDatabase Study Selection Process\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDatabases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInitial automated search results\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScreened results based on relevancy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFinal selected studies(justifying our inclusive/exclusive criteria))\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACM Digital Library\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIEEE Xplore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScience Direct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeb of Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70(86\u0026thinsp;\u0026minus;\u0026thinsp;30 duplicates)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eD. SCREENING OF PAPERS FOR INCLUSION AND EXCLUSION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research team collectively developed and established clear inclusion and exclusion criteria to determine the relevance of studies in addressing our research inquiries, as shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Comprehensive discussions and deliberations were held to ensure the accuracy and effectiveness of the criteria. It was deemed advantageous to exclude papers that merely briefly mentioned our primary focus, which is variability, particularly when such mentions were confined to introductory sentences within the abstracts.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCriteria for inclusion and exclusion\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInc\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInclusion Criteria\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInc 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWe consider journal articles and conference proceedings/papers that have undergone a peer-review process.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInc 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArticles eligible for inclusion should present substantial and relevant findings or contributions regarding the identification of emotions and personality traits in literature. These articles must clearly outline their research objectives, methodologies, and empirical results.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInc 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInclusion extends to articles where the central focus is on the identification of emotions and personality traits.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInc 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn cases where multiple studies address the same topic, we prioritize the most recent research.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInc 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe inclusion criteria cover articles published between 2019 and 2023, spanning the last decade.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExc 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArticles not written in the English language are excluded.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExc 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExcluded are articles classified as reviews, theoretical studies, conceptual papers, unpublished theses, and dissertations.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExc 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArticles addressing different aspects of emotions and personality traits, such as those within psychology, are not within the scope of our inclusion.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExc 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWe exclude articles designated as \u0026quot;In-Press\u0026quot; for the year 2024.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eE. PROCESS OF CLASSIFICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo establish our classification scheme, we followed the guidance provided by Petersen [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our research team thoroughly examined various elements of the 200 studies that were finalized for analysis, including the title, abstract, keywords, and research methodologies employed. We also assessed the research focus, publication details such as the year of publication, and geographical distribution (country of origin). Additionally, in some cases, researchers chose to delve into other sections of the documents, such as the introduction and conclusions, to gain a comprehensive understanding of the research report\u0026apos;s characteristics. To ensure a comprehensive classification, researchers often found it necessary to read the entire manuscript and, in some instances, closely examine figures and illustrations. We utilized a printed data extraction form to systematically record the attributes and relevant information from each paper. A visual representation of our classification methodology can be found in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. This process allowed us to effectively categorize and analyze the various aspects of the documents under review.\u003c/p\u003e"},{"header":"V. THE RELATIONSHIP BETWEEN EMOTIONS AND PERSONALITY TRAITS","content":"\u003cp\u003eWe delve deeper into the intricate relationship between emotions and personality traits in the context of identification methods. Emotions and personality traits, while interconnected, represent distinct psychological constructs that influence human behavior in unique ways. Here, we explore how these constructs intersect and diverge, examining their implications for research and practice in psychology and related fields.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Relationship between emotion and personality trait identification\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eEmotions and personality traits are interconnected but distinct constructs. Emotions refer to transient, short-lived states characterized by subjective feelings and physiological responses, while personality traits represent enduring patterns of behavior, cognition, and emotion.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe identification of emotions may provide valuable insights into an individual\u0026apos;s emotional temperament, which is a component of personality. Emotional temperament refers to stable individual differences in emotional reactivity and regulation, which can influence personality traits.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eConversely, personality traits can influence how individuals perceive, experience, and express emotions. For example, individuals high in neuroticism may be more prone to experiencing negative emotions, while those high in extraversion may exhibit more positive emotions.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eWhile emotions and personality traits are related, they serve different functions in understanding human behavior. Emotions provide immediate reactions to stimuli, while personality traits represent enduring characteristics that shape behavior over time.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2. Comparison of Emotion and Personality Trait Identification Methods\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe methods used for identifying emotions and personality traits may differ based on the nature of the constructs and the research objectives.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eEmotion identification methods often rely on techniques such as facial expression analysis, physiological monitoring, self-report measures, and natural language processing to assess emotional states and reactions.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIn contrast, personality trait identification methods typically involve self-report questionnaires, observer ratings, behavioral observations, and standardized assessments to measure enduring patterns of behavior, cognition, and emotion.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eWhile there may be some overlap in the methods used, such as self-report measures that capture both emotions and personality traits, the emphasis and focus of these methods differ based on the construct being assessed.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3. Differentiating Emotional Phenomena in Identification Methods\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eEmotions encompass a range of phenomena, including emotional reactions, states, temperaments, and disorders. Each of these phenomena may require different approaches for identification.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eEmotional reactions refer to immediate responses to specific stimuli and may be assessed through methods such as facial expression analysis and physiological monitoring.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eEmotional states represent transient emotional experiences and may be measured using self-report questionnaires and ecological momentary assessment methods.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eEmotional temperaments are stable individual differences in emotional reactivity and regulation and may be assessed through longitudinal studies and temperament inventories.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eEmotional (affective) disorders, such as depression and anxiety, involve disturbances in emotional functioning and may require diagnostic interviews and clinical assessments for identification.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhile emotions and personality traits are related constructs, they represent distinct aspects of human psychology. Understanding the relationship between them and the methods used for their identification is essential for advancing research in this field and developing more comprehensive models of human behavior and functioning.\u003c/p\u003e"},{"header":"VI. TAXONOMY OF TOOLS TO IDENTIFY EMOTIONS AND PERSONALITY TRAITS","content":"\u003cp\u003eIn the pursuit of understanding human emotions and personality traits through computer-based tools, it is essential to establish a taxonomy that categorizes these tools based on various criteria. This taxonomy serves as a structured framework for organizing and comprehending the diverse range of methods and technologies employed in this field, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The following section outlines a taxonomy for tools used in the identification of emotions and personality traits, taking into consideration multiple dimensions that influence their classification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Data Sources\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe first dimension in our taxonomy categorizes tools based on the data sources they utilize for emotion and personality identification. These data sources can be broadly classified into four categories:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eTextual Data\u003c/strong\u003e: Tools in this category focus on analyzing written or spoken language to extract emotional content and personality traits. Natural language processing (NLP) techniques are often employed to assess sentiment, emotion, and linguistic patterns in textual data [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eVisual Data\u003c/strong\u003e: This category includes tools that leverage visual cues, primarily facial expressions and body language, to infer emotions and, to some extent, personality traits. Deep learning models, such as convolutional neural networks (CNNs), are frequently used for facial expression recognition [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003ePhysiological Signals\u003c/strong\u003e: Tools in this category monitor physiological indicators such as heart rate, skin conductance, and EEG signals to infer emotions and physiological correlates of personality traits. Machine learning algorithms are applied to analyze these signals [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMultimodal Data\u003c/strong\u003e: Some tools combine data from multiple sources, such as text, audio, visual, and physiological signals, to enhance the accuracy and robustness of emotion and personality identification. Multimodal fusion techniques are employed for integration and analysis [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2. Machine Learning Techniques\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe second dimension of our taxonomy classifies tools based on the machine learning techniques they employ. This dimension encompasses various approaches, including the following:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eNLP\u003c/strong\u003e: Tools in this category predominantly use NLP techniques such as sentiment analysis, emotion classification, and linguistic pattern recognition to extract emotional and personality-related information from textual data [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eDeep Learning\u003c/strong\u003e: Deep learning models, including CNNs and recurrent neural networks (RNNs), are employed for tasks such as facial expression recognition, speech analysis, and multimodal data fusion, contributing to improved accuracy in emotion and personality identification [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMachine Learning Algorithms\u003c/strong\u003e: This category includes traditional machine learning algorithms such as support vector machines (SVMs), decision trees, and random forests, which are utilized in various aspects of emotion and personality analysis, including gesture recognition and physiological signal analysis [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3. Application Domains\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe third dimension of our taxonomy focuses on the domains in which these tools find application. These domains span a wide range and include but are not limited to:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003ePsychology\u003c/strong\u003e: Many tools aim to contribute to psychological research by providing insights into human emotions and personality traits. These tools aid psychologists in clinical assessments, personality profiling, and understanding of emotional responses [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eHuman\u0026ndash;Computer Interaction (HCI)\u003c/strong\u003e: In the HCI domain, tools have been developed to create more emotionally aware and responsive systems, enhancing user experiences in applications such as virtual reality, gaming, and human\u0026ndash;robot interaction [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eHealthcare\u003c/strong\u003e: Emotion and personality identification tools are applied in healthcare settings to monitor patients\u0026apos; emotional well-being and assess mental health conditions, such as depression and anxiety [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMarketing and Customer Service\u003c/strong\u003e: Tools in this category are used to analyze consumer sentiments, preferences, and personality traits for personalized marketing strategies and improved customer service [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e4. Validation Methods\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe final dimension in our taxonomy considers the validation methods used to assess the reliability and accuracy of the identified tools. This dimension encompasses diverse validation techniques, including self-report questionnaires, user studies, cross-validation, and benchmark datasets [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn summary, this taxonomy provides a structured framework for categorizing tools used in the identification of emotions and personality traits. It underscores the multidimensionality of this field, which encompasses various data sources, machine learning techniques, application domains, and validation methods. This taxonomy facilitates a deeper understanding of the evolving landscape of research and technology in the quest to uncover the intricacies of human emotions and personality characteristics [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e"},{"header":"VII. COMPARING TRADITIONAL AND COMPUTER-BASED APPROACHES TO IDENTIFYING EMOTIONS AND PERSONALITY TRAITS","content":"\u003cp\u003eWe delve into a comprehensive comparison between traditional and computer-based approaches for identifying emotions and personality traits, as shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. While the focus of our review has been primarily on the application of machine learning techniques, it is essential to acknowledge the longstanding use of traditional methods in psychological research. We aim to provide a nuanced understanding of the strengths, limitations, and suitability of both approaches.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Shortcomings of Traditional Methods\u003c/strong\u003e: Traditional identification methods often rely on manual observation, self-report measures, and expert judgment. While these methods have been valuable in psychological research, they may lack the scalability, efficiency, and objectivity offered by computer-based approaches. For example, traditional methods may struggle to process large datasets or detect subtle patterns in complex emotional and personality phenomena that machine learning algorithms can effectively capture.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e2. Effectiveness Comparison\u003c/strong\u003e: Several studies have attempted to compare the effectiveness of traditional and computer-based identification methods. These comparisons typically evaluate criteria such as accuracy, reliability, scalability, and efficiency. For instance, research may compare the accuracy of human raters in identifying emotions from facial expressions with that of computer algorithms trained on large datasets. The results from such studies can provide insights into the relative strengths and weaknesses of each approach and inform decisions regarding method selection in different contexts.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3. Limitations of Machine Learning Techniques\u003c/strong\u003e: While machine learning techniques offer significant advantages in certain contexts, they are not without limitations. One significant limitation is the reliance on labeled training data, which may introduce biases and limit the generalizability of models. Additionally, complex algorithms may lack transparency, making it challenging to interpret their decision-making processes. In cases where interpretability, transparency, or ethical considerations are paramount, researchers may prefer traditional methods that offer more straightforward and transparent approaches to identification.\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eBy thorough examination, we aim to provide researchers and practitioners with a comprehensive understanding of the strengths, weaknesses, and considerations associated with both traditional and computer-based approaches to identifying emotions and personality traits. This nuanced perspective can guide methodological choices and enhance the rigor and validity of research in this domain.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of traditional and computer-based approaches for identifying emotions and personality traits\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAspect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraditional Methods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComputer-Based Approaches\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShortcomings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-Relies on manual observation, self-report measures, and expert judgment.\u003c/p\u003e\n \u003cp\u003e- Limited scalability, efficiency, and objectivity.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- May lack transparency and interpretability.\u003c/p\u003e\n \u003cp\u003e- Require labeled training data, which can introduce biases and limit generalizability.\u003c/p\u003e\n \u003cp\u003e- Complex algorithms may be challenging to interpret.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEffectiveness Comparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- Accuracy, reliability, scalability, and efficiency may vary.\u003c/p\u003e\n \u003cp\u003e- May struggle to process large datasets or detect subtle patterns.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- Evaluation criteria include accuracy, reliability, scalability, and efficiency.\u003c/p\u003e\n \u003cp\u003e- Studies may compare human raters\u0026apos; accuracy with computer algorithms trained on large datasets.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimitations of Machine Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- Relies on labeled training data, introducing biases and limiting generalizability.\u003c/p\u003e\n \u003cp\u003e- Complex algorithms may lack transparency.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- Transparency and interpretability may be lacking.\u003c/p\u003e\n \u003cp\u003e- Dependence on labeled training data.\u003c/p\u003e\n \u003cp\u003e- Complexity may hinder interpretation.\u003c/p\u003e\n \u003cp\u003e- May not always align with ethical considerations or transparency requirements.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThis table highlights the differences between traditional and computer-based approaches for identifying emotions and personality traits, including their shortcomings, effectiveness comparison criteria, and machine learning technique limitations.\u003c/p\u003e"},{"header":"VIII. RESULTS AND FINDINGS","content":"\u003cp\u003eThe results and findings section presents the outcomes of the systematic mapping study. It provides a comprehensive overview of the identified computer-based tools for emotion and personality identification, highlighting their methodologies, effectiveness, and application domains.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnderstanding the composition and characteristics of study participants is essential for evaluating the relevance and robustness of research findings in the domain of tools designed to identify emotions and personality traits within computer science. In this section, we provide insights into the diverse range of participants involved in the studies we selected during our systematic mapping study, along with the corresponding participant numbers. The demographic information of the study participants, such as age, gender, education level, and cultural background, plays a crucial role in understanding the nuances of emotional and personality traits, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. These factors can significantly influence how individuals express emotions and manifest their personality traits in digital interactions. Our selected studies encompassed a diverse range of participant demographics, with participant numbers ranging from small cohorts of 20 to larger, more extensive samples exceeding 200[\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe size and diversity of the study sample are key considerations for the generalizability of the research findings. A larger and more diverse sample size enhances the external validity of the study results. In our selected studies, we observed variations in sample sizes. Some studies featured relatively small cohorts with approximately 30 participants, while others boasted larger, more representative samples, with participant numbers exceeding 200. Additionally, researchers often aim to include participants from various cultural backgrounds, ethnicities, and socioeconomic statuses to capture a broader range of emotional and personality variations [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. User characteristics encompass a variety of factors, including prior experience with technology, familiarity with the specific application or tool being studied, and individual differences in cognitive and emotional traits. These characteristics can significantly influence how participants engage with technology and how their emotional responses and personality traits manifest during digital interactions. Our selected studies considered a range of user characteristics, with participant numbers correlating to the specific characteristics under investigation [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eEthical considerations are of paramount importance in studies involving human participants. Ensuring ethical practices, including obtaining informed consent from participants, protecting their privacy and data, and minimizing potential harm, is crucial. The studies we selected, with their respective participant numbers, adhered to ethical guidelines and standards, reflecting a commitment to upholding the rights and well-being of the participants involved in the research [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]. Understanding the characteristics, demographics, and participant numbers of study participants is integral to interpreting and contextualizing research findings. It enables researchers and practitioners to assess the applicability of results to diverse user populations, ensuring that insights drawn from these studies are both meaningful and broadly relevant to the field of computer science.\u003c/p\u003e\n\u003cp\u003eUnderstanding the characteristics, demographics, and participant numbers of study participants is integral to interpreting and contextualizing research findings. It enables researchers and practitioners to assess the applicability of results to diverse user populations, ensuring that insights drawn from these studies are both meaningful and broadly relevant to the field of computer science.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of Study Participants and \u003cstrong\u003eStudy\u003c/strong\u003e Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAspect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudy Participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-Range of participants with numbers from 20 to over 200 [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSample Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-Varied, from approximately 30 to over 200, aiming for diverse demographics [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUser Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- Factors include prior tech experience and individual traits [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical Considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- Crucial adherence to guidelines, including consent and privacy protection [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e presents a summary of various computerized tools developed for the assessment of emotions and personality traits. In the rapidly evolving landscape of mental health and psychological research, these innovative tools utilize a range of technologies, from natural language processing to facial recognition and physiological monitoring. Each tool serves a distinct purpose, targeting specific populations and age groups while addressing various mental health concerns. The table offers a snapshot of these tools, their applications, and the key psychometric properties assessed to evaluate their effectiveness in enhancing our understanding of emotional and personality characteristics.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of \u003cstrong\u003ecomputerized tools for emotion and personality assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthor/Year\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComputerized tools proposed/used\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription of tool\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThe population of study e.g. students, university students, women, etc.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge of study participants\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy sample size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethodology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType of mental health concern\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePsychometric properties of tool assessed\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmirnov et al. (2021)[\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTitanis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA tool for intelligent text analysis in social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnglish-speaking social media users\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression, anxiety, and stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidity and Reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDe et al. (2015)[\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEigenface approach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA facial expression recognition algorithm based on the eigenface approach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian university students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;25 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMachine Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccuracy and Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExler et al. (2016)[\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWearable system for mood assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA system that uses smartphone features and data from mobile ECGs to assess mood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGerman adults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u0026ndash;46 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMobile App Data Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidity and User Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOvur et al. (2021)[\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutonomous learning framework for sEMG-based hand gesture recognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA machine learning framework that uses surface electromyography (sEMG) and depth data to recognize hand gestures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthy adults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u0026ndash;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMachine Learning and Gesture Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecision and Recall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAzam et al. (2019)[\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmartphone-based mindful breathing app\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA smartphone-based app that guides users through mindful breathing exercises\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUniversity students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;25 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandomized controlled trial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression and anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReliability and Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNave et al. (2018)[\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial media mobile photography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsing social media mobile photography to self-track emotional states\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneral Smartphone Users\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;25 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMobile App Data Collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional Well-being\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTest-retest Reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVillatoro-Tello et al. (2021)[\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical interviews as a support tool for depression detection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpeech analysis tool for emotion detection in clinical interviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMachine learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMood Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity and Specificity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCheong et al. 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(2019)[\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoachAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA conversational agent assisted health coaching platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneral Smartphone Users\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;25 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural Language Processing (NLP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional Well-being\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidity and User Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetin et al. (2022)[\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep learning method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA deep learning method that uses EEG data to differentiate patients with bipolar disorder from controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatients with Bipolar Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectroencephalography (EEG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBipolar Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTest-retest Reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCuijpers et al. (2016)[\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternet-based cognitive behavioral therapy (iCBT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA self-guided, computer-based program that teaches CBT skills for depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdults with depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;65 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandomized controlled trial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEfficacy, acceptability, and usability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndrews et al. (2018)[\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVirtual reality exposure therapy (VRET)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA computer-generated simulation that exposes people to feared situations in a safe and controlled environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdults with anxiety disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;65 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandomized controlled trial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEfficacy, acceptability, and usability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWolf et al. (2016)[\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMobile phone intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA mobile phone intervention that provides guided mindfulness exercises to people with depressive symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdults with depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;65 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEfficacy, acceptability, and usability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOverall, the results and findings provide a comprehensive synthesis of the current state of research on computer-based tools employing machine learning techniques for emotion and personality identification. This section highlights the strengths and limitations of the identified tools, guiding researchers and practitioners in developing more accurate and reliable means of understanding human emotions and personality traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ1: What are the existing challenges of developing tools for identifying emotions and personality traits?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentifying emotions and personality traits using computer-based tools is a burgeoning field with numerous challenges. These challenges span technological, ethical, and psychological domains, reflecting the complexity of capturing and interpreting human emotions and personality traits through digital means. Below, we delve into the existing challenges faced in the development of such tools, supported by relevant references.\u003c/p\u003e\n\u003cp\u003eObtaining high-quality and diverse datasets for training machine learning models is a fundamental challenge. Biases in training data, limited sample sizes, and data privacy concerns can hinder the development of accurate emotion and personality detection tools. Emotions and personality are multidimensional constructs, and integrating insights from psychology, neuroscience, and computer science is challenging. Ensuring that tools accurately capture these complex aspects of human behavior is an ongoing struggle. Emotions and personality traits can vary across cultures, leading to the challenge of developing tools that are culturally sensitive and applicable to a global audience [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAchieving real-time emotion detection and personality assessment remains a challenge, particularly in contexts where timely responses are critical, such as mental health applications [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e]. Gathering sensitive emotional and personality data raises significant ethical questions. Safeguarding user privacy, obtaining informed consent, and protecting against data breaches are essential but challenging aspects of tool development [\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e]. Emotions are inherently subjective, making it difficult to create objective and universally applicable measurement tools. The challenge lies in developing methods that can reliably interpret subjective emotional experiences [\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e]. Integrating data from various sources, such as text, facial expressions, physiological signals, and voice, into a cohesive assessment of emotions and personality traits is a complex challenge requiring advanced multimodal fusion techniques [\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e]. Ensuring the validity and generalizability of tools across diverse populations and contexts is a continual challenge. Validation methodologies need to evolve to encompass the intricacies of emotions and personality [\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e]. The development of tools that users find acceptable and easy to use is vital for adoption. The challenge is in designing interfaces and interactions that are user-friendly and nonintrusive [\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e]. Machine learning models trained on biased data can perpetuate stereotypes and inequalities. Ensuring fairness in emotion and personality assessment tools is an ongoing challenge [\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe development of tools for identifying emotions and personality traits is a multifaceted endeavor that involves technological, ethical, and psychological complexities. Addressing these challenges requires collaboration across disciplines, rigorous data collection and analysis, ethical considerations, and a commitment to ongoing improvement in tool design and validation. The following table summarizes the existing challenges of developing tools for identifying emotions and personality traits.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eChallenges in Developing Tools for Identifying Emotions and Personality Traits\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChallenge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObtaining high-quality and diverse datasets for training machine learning models\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiases in training data, limited sample sizes, and data privacy concerns can hinder the development of accurate emotion and personality detection tools.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntegrating insights from psychology, neuroscience, and computer science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotions and personality traits are multidimensional constructs, and integrating insights from various disciplines is challenging.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCultural sensitivity and global applicability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotions and personality traits can vary across cultures, leading to the challenge of developing tools that are culturally sensitive and applicable to a global audience.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReal-time emotion detection and personality assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAchieving real-time detection and assessment, particularly in critical contexts like mental health applications, remains challenging.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical considerations and data privacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSafeguarding user privacy, obtaining informed consent, and protecting against data breaches are essential but challenging aspects of tool development.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubjectivity of emotions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotions are inherently subjective, making it difficult to create objective and universally applicable measurement tools.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultimodal fusion techniques\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntegrating data from various sources into a cohesive assessment of emotions and personality traits requires advanced multimodal fusion techniques.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidity and generalizability across diverse populations and contexts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnsuring the validity and generalizability of tools across diverse populations and contexts is a continual challenge.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUser acceptance and usability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeveloping tools that users find acceptable and easy to use is vital for adoption. The challenge is in designing user-friendly and nonintrusive interfaces and interactions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness in machine learning models\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMachine learning models trained on biased data can perpetuate stereotypes and inequalities. Ensuring fairness in emotion and personality assessment tools is an ongoing challenge.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e provides a concise overview of the challenges encountered in the development of tools for identifying emotions and personality traits, along with relevant references supporting each challenge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ1.1 What are the data collection methods used in identifying emotions and personality traits?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn studies aimed at identifying emotions and personality traits, various data collection methods are employed to gather the necessary information for analysis. These methods are chosen based on the research objectives, the nature of the traits being studied, and the available resources. There are several common data collection methods used in such studies.\u003c/p\u003e\n\u003cp\u003eSurveys and questionnaires are widely used to collect self-reported data on personality traits and emotional experiences. Participants answered a series of standardized questions designed to assess their personality characteristics or emotional states. Examples include the Big Five Inventory for Personality Traits and the Positive and Negative Affect Schedule (PANAS) for emotions [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Interviews, including structured, semistructured, and open-ended formats, allow researchers to gather in-depth information about an individual\u0026apos;s emotions and personality. Clinical interviews, for instance, are used in psychological assessments to diagnose personality disorders [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eBehavioral observations involve systematically watching and recording an individual\u0026apos;s actions, facial expressions, body language, and verbal cues to infer their emotional states and personality traits. This method is often used in clinical and research settings. Physiological data collection methods, such as electrocardiography (ECG), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI), are used to monitor physiological responses associated with emotions [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e]. These measures can provide objective data on emotional reactions. Textual data sources, such as social media posts, emails, or written essays, can be analyzed using natural language processing (NLP) techniques to extract emotional content and personality traits. Sentiment analysis and linguistic analysis are common approaches. Facial expression analysis involves using computer vision techniques to recognize and analyze facial expressions in images or videos [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]. Facial expression analysis is a valuable method for assessing emotions noninvasively.\u003c/p\u003e\n\u003cp\u003eExperimental tasks and games are designed to elicit specific emotional responses or behaviors from participants. These tasks are often used in psychology and neuroscience studies to study emotions in controlled settings. Wearable sensors, such as heart rate monitors and skin conductance sensors, can capture physiological changes associated with emotions in real time [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. These sensors are used in both research and clinical applications. Mobile applications and smart devices with built-in sensors, such as accelerometers and GPSs, can collect data on users\u0026apos; behaviors, movements, and locations, which can be indicative of emotions and personality traits. Biometric data, including fingerprints, iris scans, and voice recordings, can be used to identify unique characteristics related to personality traits and emotional states [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe choice of data collection method depends on the specific research goals, the target population, and the feasibility of the approach. Many studies employ a combination of these methods to obtain a comprehensive understanding of emotions and personality traits. In Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, we outline and discuss the diverse array of data collection methods commonly utilized in studies aimed at identifying emotions and personality traits. Each method offers unique insights and advantages, contributing to a comprehensive understanding of human behavior and psychological processes.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eData collection methods for identifying emotions and personality traits\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData Collection Method\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExample Studies\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurveys and Questionnaires\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipants answer standardized questions to assess personality traits or emotional states.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStructured or semistructured interviews gather in-depth information about emotions and personality.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBehavioral Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystematic observation of actions, facial expressions, and verbal cues to infer emotions and traits.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysiological Data Collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonitoring physiological responses such as ECG, EEG, and fMRI to assess emotions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTextual Data Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnalyzing social media posts or written text to extract emotional content and personality traits using NLP.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFacial Expression Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsing computer vision techniques to recognize and analyze facial expressions in images or video.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental Tasks and Games\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDesigned tasks to elicit specific emotional responses in controlled settings.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWearable Sensors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsing sensors like heart rate monitors to capture physiological changes associated with emotions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMobile Applications and Smart Devices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollecting behavioral data, movements, and locations from mobile apps and smart devices.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiometric Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUtilizing biometric data like fingerprints and voice recordings to identify unique characteristics related to emotions and traits.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe methods shown in Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e offer various ways to gather data on emotions and personality traits, providing researchers with diverse approaches to studying these phenomena.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ1.2 What are the data analysis methods used in identifying emotions and personality traits studies?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentifying emotions and personality traits in studies involves the use of various data analysis methods to process and make sense of the collected data. The choice of analysis methods depends on the nature of the data, the research objectives, and the complexity of the traits being studied. There are several common data analysis methods used in such studies.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics, such as the mean, median, and standard deviation, are used to summarize and describe the central tendencies and distributions of personality trait scores or emotional responses obtained from surveys or questionnaires [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Factor analysis is employed to identify underlying factors or dimensions within a set of observed variables. In personality research, the Big Five personality traits (i.e., openness, conscientiousness, extraversion, agreeableness, neuroticism) are often extracted from a pool of related questionnaire items [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eCluster analysis groups individuals with similar personality profiles or emotional responses into clusters or categories. It can help individuals identify distinct personality types or emotional patterns within a population [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]. Regression analysis is used to explore relationships between personality traits or emotional states and other variables. For example, it can be used to examine how personality traits predict specific behaviors or outcomes [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eMachine learning techniques, including classification and regression algorithms, are increasingly used to predict and classify emotions and personality traits based on various data sources. Support vector machines, decision trees, and neural networks are common choices [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]. NLP methods are employed to analyze textual data, such as social media posts or written content, to extract emotional content, sentiment, or personality traits. Techniques include sentiment analysis and text classification [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eContent analysis involves systematically coding and categorizing qualitative data, such as interview transcripts or open-ended survey responses, to identify themes and patterns related to emotions and personality [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]. In studies that involve neuroimaging data (e.g., fMRI, EEG), sophisticated image analysis techniques are used to identify brain regions associated with specific emotions or personality traits [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn facial expression analysis, machine vision algorithms are used to detect and classify facial expressions, helping to identify emotional states based on facial cues [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]. Time series analysis is applied when studying how emotions or personality traits change over time. It can reveal temporal patterns and trends in emotional responses [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]. SEM is used to test complex models that involve multiple variables and relationships. It can be used to examine how personality traits interact and influence outcomes [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eBiometric data, such as heart rate variability or skin conductance, are analyzed to identify physiological patterns associated with specific emotional states. Data visualization techniques, including charts, graphs, and heatmaps, are used to present and interpret complex data related to emotions and personality traits [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe choice of data analysis methods depends on the research goals and the type of data collected. Many studies combine multiple methods to gain a comprehensive understanding of emotions and personality traits, especially when using diverse data sources such as surveys, physiological measures, and textual data.\u003c/p\u003e\n\u003cp\u003eThe following table summarizes the data analysis methods used in identifying emotions and personality trait studies:\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of Data Analysis Methods for Identifying Emotions and Personality Traits\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData Analysis Method\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescriptive Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSummarizes central tendencies and distributions of personality trait scores or emotional responses obtained from surveys or questionnaires.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFactor Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIdentifies underlying factors or dimensions within observed variables, often used to extract the Big Five personality traits from related questionnaire items.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroups individuals with similar personality profiles or emotional responses into clusters or categories, aiding in identifying distinct personality types.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegression Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExplores relationships between personality traits or emotional states and other variables, predicting specific behaviors or outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMachine Learning Techniques\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredicts and classifies emotions and personality traits based on various data sources, including support vector machines, decision trees, and neural networks.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural Language Processing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnalyzes textual data, such as social media posts or written content, extracting emotional content, sentiment, or personality traits using sentiment analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContent Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystematically codes and categorizes qualitative data, such as interview transcripts or open-ended survey responses, identifying themes related to emotions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeuroimaging Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUtilizes sophisticated image analysis techniques to identify brain regions associated with specific emotions or personality traits.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFacial Expression Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmploys machine vision algorithms to detect and classify facial expressions, aiding in identifying emotional states based on facial cues.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime Series Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExamines how emotions or personality traits change over time, revealing temporal patterns and trends in emotional responses.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStructural Equation Modeling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTests complex models involving multiple variables and relationships, examining how personality traits interact and influence outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiometric Data Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnalyzes physiological patterns associated with specific emotional states, including heart rate variability or skin conductance.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData Visualization Techniques\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresents and interprets complex data related to emotions and personality traits using charts, graphs, and heatmaps.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e provides an overview of the various data analysis methods employed in studies focused on identifying emotions and personality traits, highlighting the diverse approaches used to gain insights into human behavior and psychological processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ2 - What are the key open issues in developing tools for identifying emotions and personality traits?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentifying emotions and personality traits through tools and technologies is an evolving field, and several key open issues persist. These challenges reflect the complexity of the human psyche and the dynamic nature of emotions and personality traits. There are several key open issues.\u003c/p\u003e\n\u003cp\u003eEffective collaboration between psychologists, computer scientists, neuroscientists, and other experts is essential. Bridging the gap between these disciplines remains a challenge, as each discipline provides unique insights into the study of emotions and personality [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. Existing tools may not accurately capture emotions and personality traits across diverse cultural contexts. The development of culturally sensitive assessment tools and the accounting for cultural variations remain open issues [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eReal-time emotion detection and personality assessment are crucial for applications such as mental health support and human-computer interaction. The development of tools that can provide timely and accurate assessments in dynamic environments is a challenge [\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e]. The collection of personal data related to emotions and personality raises significant ethical questions. Balancing the need for data with privacy and ethical considerations remains an ongoing issue [\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eEmotions are inherently subjective and influenced by context [\u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e]. The development of tools that can account for individual subjectivity and situational context is a complex challenge. Integrating data from various sources, such as text, facial expressions, physiological signals, and voice, into a coherent assessment of emotions and personality traits is an open issue. Advanced multimodal fusion techniques are needed [\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eEnsuring the validity and generalizability of assessment tools across diverse populations and contexts is an ongoing challenge [\u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e]. Validation methodologies must evolve to encompass the intricacies of emotions and personality. Machine learning models trained on biased data can perpetuate stereotypes and inequalities [\u003cspan class=\"CitationRef\"\u003e91\u003c/span\u003e]. Ensuring fairness in emotion and personality assessment tools is a critical issue that requires attention. The development of tools that users find acceptable and easy to use is vital for adoption. The challenge is in designing interfaces and interactions that are user-friendly and nonintrusive [\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eUnderstanding how emotions and personality traits change over time and in response to interventions or life events is a significant open issue. Longitudinal studies are needed to address this aspect. Creating tools that can provide personalized insights into emotions and personality traits for individuals is an emerging challenge. Personalization requires the integration of diverse data sources and adaptive algorithms. Ensuring that AI-based tools for emotion and personality assessment adhere to ethical principles, such as transparency, accountability, and fairness, is a pressing issue [\u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eExtending the use of emotion and personality assessment tools to domains beyond psychology, such as healthcare, education, and marketing, poses open challenges in adapting and validating these tools for new contexts. Combining the outputs of automated tools with human judgment and expertise is a complex issue [\u003cspan class=\"CitationRef\"\u003e95\u003c/span\u003e]. The development of hybrid systems that leverage both automated and human assessments is an open area of research. Emotions are dynamic and can change rapidly. Modeling these dynamics and their impact on decision-making and behavior is a challenging research problem [\u003cspan class=\"CitationRef\"\u003e96\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAddressing these open issues in the development of tools for identifying emotions and personality traits will require ongoing collaboration, multidisciplinary approaches, and innovative research across psychology, computer science, and related fields.\u003c/p\u003e\n\u003cp\u003eThe following table summarizes the key open issues in developing tools for identifying emotions and personality traits:\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eKey Open Issues in Developing Tools for Identifying Emotions and Personality Traits\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOpen Issue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEffective Collaboration between Disciplines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBridging the gap between psychologists, computer scientists, neuroscientists, and other experts to leverage diverse insights into emotions and personality.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCultural Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeveloping assessment tools that accurately capture emotions and personality traits across diverse cultural contexts.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReal-time Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeveloping tools capable of providing timely and accurate assessments of emotions and personality traits in dynamic environments, such as mental health support and HCI applications.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical Considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBalancing the collection of personal data related to emotions and personality with privacy and ethical considerations.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubjectivity and Context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeveloping tools that account for individual subjectivity and situational context in assessing emotions and personality traits.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultimodal Fusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntegrating data from various sources (e.g., text, facial expressions, physiological signals) into a coherent assessment of emotions and personality traits.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidity and Generalizability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnsuring the validity and generalizability of assessment tools across diverse populations and contexts.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAddressing biases in machine learning models used for emotion and personality assessment to ensure fairness.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUser Acceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDesigning user-friendly interfaces and interactions for emotion and personality assessment tools.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLongitudinal Studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConducting longitudinal studies to understand how emotions and personality traits change over time and in response to interventions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeveloping tools that provide personalized insights into emotions and personality traits for individuals.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnsuring AI-based tools adhere to ethical principles such as transparency, accountability, and fairness.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtending Applications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdapting and validating emotion and personality assessment tools for new contexts beyond psychology.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid Systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeveloping hybrid systems that combine automated and human assessment for more accurate results.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModeling Dynamics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModeling the dynamic nature of emotions and their impact on decision-making and behavior.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e provides a structured overview of the key open issues, allowing for easy reference and understanding of the challenges in developing tools for identifying emotions and personality traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ3-What types of contributions have been proposed in this research field?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the research field of identifying emotions and personality traits using computer-based tools, various types of contributions have been proposed. These contributions encompass a wide range of advancements, innovations, and applications. There are some common types of contributions made in this field.\u003c/p\u003e\n\u003cp\u003eResearchers have proposed novel algorithms and machine learning models for accurately detecting emotions and assessing personality traits. These advancements have led to more robust and reliable tools for automated analysis [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]. Contributions include the development of effective feature extraction methods for different data sources, such as text, speech, facial expressions, and physiological signals. These methods improve the quality of the input data for analysis [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eMany contributions have focused on integrating data from multiple sources (e.g., text, audio, video) to provide a holistic understanding of emotions and personality traits. Cross-modal fusion techniques have been proposed for this purpose [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Researchers have created and shared large datasets containing emotional and personality data, enabling the development and validation of new tools. These databases have contributed to the advancement of the field [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe contributions of this study include rigorous validation studies that assess the accuracy and reliability of emotion and personality assessment tools. Validation ensures that the tools are suitable for various applications [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Research has been conducted to investigate the cross-cultural applicability of emotion and personality assessment tools. Understanding cultural variations is essential for creating universally valid tools [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eContributions have been made in applying emotion and personality assessment tools to healthcare contexts. These tools help in diagnosing and monitoring mental health conditions and providing personalized interventions [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. In the field of HCIs, contributions involve the development of user interfaces and systems that adapt based on users\u0026apos; emotional states and personality traits, enhancing user experiences [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eResearchers have proposed personalized approaches to emotion and personality assessment, tailoring recommendations and interventions based on individual profiles [\u003cspan class=\"CitationRef\"\u003e97\u003c/span\u003e]. Ethical contributions address the responsible use of tools for emotion and personality assessment. Ethical guidelines and frameworks help ensure user privacy and data security [\u003cspan class=\"CitationRef\"\u003e98\u003c/span\u003e]. Contributions extend to commercial applications, where emotion and personality assessment tools are integrated into marketing, customer service, and product design to enhance user engagement and satisfaction [\u003cspan class=\"CitationRef\"\u003e99\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTools for identifying emotions and personality traits are applied in education and training settings to tailor instructional content and support personalized learning experiences [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]. This field advances psychological research by providing new methods and tools for studying emotions and personality traits in controlled and real-world settings. Researchers have proposed predictive models that use emotion and personality data to forecast behaviors, such as consumer choices, social interactions, and mental health outcomes [\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eContributions include fostering collaboration between psychologists, computer scientists, neuroscientists, and other experts, leading to a more holistic understanding of emotions and personality. The development of open-source software and libraries for emotion and personality analysis allows for broader access and collaboration within the research community [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThese types of contributions collectively contribute to the advancement of the field of identifying emotions and personality traits, enabling its application in various domains and addressing complex challenges. The ongoing collaboration between researchers and practitioners continues to drive innovation in this interdisciplinary field.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab12\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTypes of Contributions in the Research Field of Identifying Emotions and Personality Traits\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType of Contribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNovel Algorithms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResearchers propose innovative algorithms and models for emotion and personality assessment, enhancing automated analysis..\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData Integration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEfforts focus on integrating data from various sources to provide a holistic understanding of emotions and personality traits, contributing to comprehensive analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation Studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContributions include rigorous validation studies to assess the accuracy and reliability of emotion and personality assessment tools, enhancing their credibility.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-Cultural Research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResearch investigates the cross-cultural applicability of emotion and personality assessment tools, aiming to ensure universal validity and inclusivity.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthcare Applications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContributions involve the application of emotion and personality assessment tools in healthcare settings, enhancing patient care and mental health treatment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCI Enhancements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResearch focuses on developing user interfaces and systems that adapt based on users\u0026apos; emotional states and personality traits, improving user experiences.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e91\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResearchers propose personalized approaches to emotion and personality assessment, tailoring interventions based on individual profiles.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthical Considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContributions address ethical considerations in emotion and personality assessment, promoting responsible usage and data security.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommercial Applications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTools for identifying emotions and personality traits are integrated into commercial applications to enhance user engagement and satisfaction.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation Applications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContributions extend to education settings, where tools are used to tailor instructional content and support personalized learning experiences.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e95\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e summarizes the various types of contributions made in the research field of identifying emotions and personality traits using computer-based tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ4- What is the most focal topic of the publication trend in the studies identifying emotions and personality traits during the last five years from the selected studies?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe most focal topic of the publication trend in studies identifying emotions and personality traits during the last five years has been the use of machine learning and artificial intelligence (AI) [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. This is evidenced by the rapid increase in the number of publications using these methods, as well as the increasing sophistication of the models being developed.\u003c/p\u003e\n\u003cp\u003eOne of the main advantages of using machine learning and AI for emotion and personality recognition is that they can be used to analyze large amounts of data quickly and accurately. This is important because emotions and personality traits can be difficult to identify manually, especially in real time. Another advantage of using machine learning and AI is that they can be used to analyze a wide variety of data types, including text, speech, images, and videos. This allows researchers to develop more comprehensive and accurate models of emotion and personality recognition. Some specific examples of the use\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab13\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePublication trends in studies identifying emotions and personality traits\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMost Focal Topic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUse of Machine Learning and AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDominant trend in recent research on identifying emotions and personality traits, characterized by the increasing use of machine learning and artificial intelligence (AI) methods.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e96\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdvantages of Machine Learning and AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighlights the benefits of using machine learning and AI for emotion and personality recognition, including the ability to analyze large datasets quickly and accurately, and the versatility in handling various data types.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e97\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecific Examples of Machine Learning and AI Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvides examples of machine learning applications in identifying emotions and personality traits, such as facial expression analysis, text and speech analysis, and image and video analysis, with diverse applications across different fields.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e98\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotential Applications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiscusses the potential applications of machine learning and AI in emotion and personality identification, including human-computer interaction, customer service, mental health diagnosis, and crime prevention, indicating the wide-ranging impact of these technologies.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e99\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eof machine learning and AI for emotion and personality recognition include the following:\u003c/p\u003e\n\u003cp\u003eMachine learning can be used to identify facial expressions, which are key indicators of emotion. For example, a machine learning model could be trained to identify the facial expressions associated with happiness, sadness, anger, and fear [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Machine learning can be used to analyze text for emotional cues, such as the use of certain words or phrases. For example, a machine learning model could be trained to identify tweets that express happiness, sadness, anger, or fear [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Machine learning can be used to analyze speech for emotional cues, such as the tone of voice, pitch, and rhythm of speech. For example, a machine learning model could be trained to identify phone calls where the caller is expressing anger or distress [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Machine learning can be used to analyze images for emotional cues, such as the expressions of people\u0026apos;s faces or the body language of people in a scene. For example, a machine learning model could be trained to identify images that depict happiness, sadness, anger, or fear [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Machine learning can be used to analyze videos for emotional cues, such as facial expressions, body language, and tone of voice. For example, a machine learning model could be used to identify videos where people express happiness, sadness, anger, or fear [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The use of machine learning and AI for emotion and personality recognition has a wide range of potential applications. For example, it could be used to develop more engaging and personalized human-computer interaction systems, to improve customer service, to develop new diagnostic tools for mental health disorders, and to develop new ways to detect and prevent crime.\u003c/p\u003e\n\u003cp\u003eOverall, the publication trend in studies identifying emotions and personality traits during the last five years has been marked by the increasing use of machine learning and AI, as well as the development of new and improved methods for data collection, labeling, and ethical considerations.\u003c/p\u003e\n\u003cp\u003eThese findings provide insights into the increasing prominence of machine learning and AI in the study of identifying emotions and personality traits during the last five years, as well as the wide range of potential applications and benefits associated with these technologies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. CHANNEL OF PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;channel of publication\u0026quot; refers to the specific venue or platform through which research findings, papers, articles, and other scholarly works are made publicly available to the academic community and the broader public. The choice of publication channel can significantly impact the visibility, accessibility, and credibility of research. Here, we will discuss the concept of the publication channel in more detail:\u003c/p\u003e\n\u003cp\u003ePublishing the research findings in reputable academic journals is a common and respected channel for disseminating research in various fields. In this case, the study could be submitted to journals that focus on topics related to artificial intelligence, machine learning, affective computing, psychology, human-computer interaction, or any field closely related to the research. These journals typically require rigorous peer review, ensuring the quality and credibility of the research. Presenting the research at conferences and subsequently publishing the proceedings is common in many academic and technical fields. Depending on the subject matter, conferences related to artificial intelligence, machine learning, emotion analysis, and personality assessment may be suitable. Conference papers offer a platform for presenting research to a specialized audience and can facilitate discussions and feedback from experts in the field. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e shows the number of systematic mapping studies published from 2019 to 2023.\u003c/p\u003e\n\u003cp\u003eIn-depth studies or comprehensive reviews are often published as books or book chapters. The research could be expanded into a book or contribute a chapter to an edited volume related to the study\u0026apos;s focus. This allows for a more extensive exploration of the topic and provides a reference for scholars and practitioners in the field. If the research is part of a doctoral or master\u0026apos;s thesis, it can be made accessible through the university\u0026apos;s library and online repository. This channel is particularly relevant if the research is conducted within an academic institution. The choice of publication channel should align with the research\u0026apos;s objectives, target audience, and the level of detail and rigor needed. Researchers may also consider multiple channels to reach different audiences and maximize the impact of their findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. PUBLICATION TREND\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe publication trend for systematic mapping studies of tools to identify emotions and personality traits from 2019 to 2023 shows a steady increase in the number of publications each year. This trend is likely due to a number\u003c/p\u003e\n\u003cp\u003eof factors. The availability of data on emotions and personality traits is increasing. These data are collected from a variety of sources, including social media, wearable devices, and surveys. The increasing sophistication of machine learning and AI algorithms. These algorithms can be used to analyze large amounts of data to identify patterns and trends that are difficult or impossible to identify manually. There is growing interest in the use of emotion and personality recognition in a variety of applications, such as human-computer interaction, customer service, mental health, and security. Figure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e shows the number of systematic mapping studies on tools for identifying emotions and personality traits published each year from 2019 to 2023.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, the number of publications increased from 25 in 2019 to 60 in 2023. This represents a fivefold increase over the five-year period. The following are some of the key findings from the systematic mapping studies published during this period. The majority of the tools surveyed use machine learning and AI algorithms to identify emotions and personality traits [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. The most common data types used to train machine learning models are text, speech, and images [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. The most common applications of these tools are human-computer interaction, customer service, mental health, and security. The publication trend for systematic mapping studies on tools to identify emotions and personality traits suggests that this is a rapidly growing field with a wide range of potential applications.\u003c/p\u003e"},{"header":"IX. DISCUSSION","content":"\u003cp\u003eBy comparing and contrasting the different data sources utilized for emotion and personality identification, our study delves into the unique methodologies and machine learning techniques employed across various categories, including text analysis, facial expression recognition, gesture analysis, and physiological signal monitoring [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Through this analysis, we elucidate the strengths and limitations of each approach, considering factors such as accuracy, reliability, and scalability. Furthermore, we thoroughly examined the validity and reliability of the identified tools, critically assessing the appropriateness of the validation methods and potential sources of bias inherent in each technique. Furthermore, this study explored the practical applications and use cases of these tools in diverse domains, such as marketing, healthcare, and personalized systems. By highlighting real-world scenarios where these tools can offer valuable insights and support decision-making processes, we underscore their potential impact on improving individual well-being and enhancing user experiences [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Additionally, we address ethical considerations and privacy concerns associated with the use of sensitive data for emotion and personality identification, emphasizing the importance of informed consent and responsible data handling practices. Moreover, our study delves into the challenges and opportunities for future research in this domain. We advocate for a focus on cross-cultural validation, emphasizing the need for tools that are sensitive to cultural nuances and applicable across diverse populations [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Additionally, we highlight the importance of real-time emotion and personality recognition, envisioning the development of systems capable of dynamically adapting to users' changing emotional states and personality traits. Furthermore, we advocate for multimodal approaches that integrate various data sources, recognizing the potential for synergy and improved accuracy in combining textual, visual, and physiological signals [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, this study underscores the significance of computer-based tools in advancing our understanding of human emotions and personality traits. However, we emphasize the need for robust validation methods and ethical considerations to ensure the reliability and credibility of these applications. By addressing these challenges and embracing future research directions, we anticipate that advancements in this field will contribute to more accurate and reliable tools for understanding and supporting human emotions and personality traits in diverse contexts.\u003c/p\u003e"},{"header":"X. CONCLUSION","content":"\u003cp\u003eIn conclusion, this systematic mapping study provides a comprehensive overview of the current state of research on computer-based tools employing machine learning techniques for emotion and personality identification. The findings highlight the significance of emotions and personality traits in shaping human behavior, cognition, and overall well-being. The research landscape reveals a diverse range of data sources, including text analysis, facial expression recognition, gestures, and physiological signals, harnessed by sophisticated machine learning algorithms to gain deeper insights into an individual's psychological makeup.\u003c/p\u003e \u003cp\u003eThe results underscore the effectiveness of natural language processing techniques in capturing emotions from textual data, while advanced deep learning models demonstrate remarkable accuracy in facial expression recognition. Moreover, gesture recognition and physiological signal analysis using machine learning algorithms offer promising avenues for understanding personality traits and emotional responses. However, the discussion also highlights the importance of standardized validation procedures and considerations of potential biases to ensure the credibility and reliability of the identified tools.\u003c/p\u003e \u003cp\u003eThe systematic mapping study presented here provides valuable insights for researchers, practitioners, and developers interested in leveraging machine learning to gain a deeper understanding of human emotions and personality characteristics. By considering the strengths, limitations, and ethical considerations of these tools, we can ensure responsible and impactful applications that benefit individuals and society as a whole. As technology continues to evolve, the development of empathetic and personalized systems can lead to enhanced user experiences, improved mental health interventions, and a deeper understanding of what it means to be human.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by the Postgraduate Research Grants (PPP)-PG169-2024A and PG005-2024B from the University of Malaya, Malaysia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDuring the preparation of this work\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e\u003cem\u003e\u0026nbsp;we used GPT-3.5, Google Bard, Quillbot and Grammarly to improve the quality of writing. 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Machine learning algorithms for detection and classifications of emotions in contact center applications. \u003cem\u003eSensors\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(14), 5311.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Malaya","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Systematic Mapping Study, Emotion Traits, Personality Traits, Artificial Intelligence, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-4356776/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4356776/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEmotions and personality traits profoundly influence human behavior and well-being. Recent advancements in computer-based tools utilizing machine learning techniques have opened new avenues for identifying and understanding these psychological aspects in individuals. This systematic mapping study comprehensively reviews research articles from reputable journals, focusing on tools that leverage various data sources, such as text analysis, face recognition, gestures, and heart rate monitoring. The selected papers underwent rigorous analysis, leading to the categorization of identified tools based on their methodologies, objectives, and application domains. Natural language processing techniques were found to excel in capturing emotions from textual data, while deep learning models demonstrated accuracy in face recognition. Machine learning algorithms showed promise in analyzing gestures and heart rate to understand personality traits and emotional responses. However, the study also highlights the importance of validation standardization and large-scale studies across diverse populations to enhance the reliability and effectiveness of these tools.\u003c/p\u003e","manuscriptTitle":"Systematic Mapping Study of Tools to Identify Emotions and Personality Traits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 16:45:24","doi":"10.21203/rs.3.rs-4356776/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48c86d13-5890-4ade-b76b-4f10175b89ec","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31865274,"name":"Theoretical Computer Science"}],"tags":[],"updatedAt":"2024-05-07T16:45:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-07 16:45:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4356776","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4356776","identity":"rs-4356776","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}