Social Media User Evaluation for Quantum Computing Technology Via Sentiment Analysis

Social Media User Evaluation for Quantum Computing Technology Via Sentiment Analysis | 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 Research Article Social Media User Evaluation for Quantum Computing Technology Via Sentiment Analysis Adel Assiri, Abdu Gumaei, Faisal Mehmood, Sami Ullah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3999636/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 Sentiment analysis is one of the most well-known applications of natural language processing (NLP) techniques used to determine a text's sentiment or emotional tone, such as a sentence, a paragraph, or an entire document. The goal of sentiment analysis is to identify and extract the underlying sentiment expressed by the author, whether positive or negative. Social media platforms like Twitter, Facebook, and Google + are quickly gaining popularity due to the ability for users to share and express their opinions on many subjects, engage in conversation with different organizations, and broadcast messages globally. Sentiment analysis has been extensively studied to track and understand developer comments and views. Quantum software engineering develops software for quantum computers, which use quantum computing to process data. It has gained significant prominence in the field of software technology. Quantum computing may tackle issues that classical computers cannot, advancing cryptography, optimization, and material science. This study aims to explore the social media user review for quantum computing technology innovation in the current era. For this purpose, sentiment analysis applies to social media user reviews for quantum computing technology use. The extracted data is scrubbed through preprocessing techniques. TextBlob, VADER, and supervised learning classification methods have analyzed the sentiments and topics extracted from social media. Results show that quantum users are satisfied with using this soft computing technology and find this experience a successful, positive review for innovative quantum computing technology. Quantum Computing Technology Sentiment Analysis TextBlob VADER Supervised Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Quantum computing leverages principles like superposition and entanglement from quantum mechanics to process information in quantum bits (qubits) [ 1 ]. Superposition enables qubits to exist in multiple states simultaneously, exponentially increasing computational possibilities. Entanglement links qubits, allowing them to be correlated, even at a distance. This unique capability enhances computational power. Quantum computing's potential lies in revolutionizing fields such as cryptography, where it can break current encryption methods, optimization by solving complex problems more efficiently, and material science for simulating molecular structures and properties at a profound level [ 2 , 3 ]. Currently, individuals express their thoughts and opinions differently through the internet. Nowadays, it is usually done through blog posts, internet forums, product review websites, social media, etc. [ 4 ]. Millions of people today use social networking sites such as Facebook, Twitter, Google Plus, and others to share their ideas and opinions about everyday life. The interactive media offered by social networking sites allows users to use forums to educate and persuade others. Sentiment analysis data is abundant in tweets, status updates, blog posts, comments, reviews, and other social media material. In addition, social media allows companies to reach the people they want and promote their products or services [ 5 ]. People heavily rely on online user-generated content when making decisions. For instance, before deciding to purchase a product or pay for a service, people often read about it online and discuss it via social media. There is not enough user-generated content for each user to process. Various sentiment analysis techniques are in use because the process must be automated [ 6 ]. Sentiment analysis (SA) helps users to access product information's satisfaction level before purchasing. Marketers and organizations use analytical data to get information about their goods or services, enabling them to tailor their offerings to meet users' needs and preferences [ 7 ]. The primary goals of textual information retrieval techniques are processing, looking for, or examining the material already available. Although there is an objective component to the facts, other aspects of literature are inherently subjective. SA, which studies views, feelings, assessments, attitudes, and emotions, depends mainly on these contents. Due to the vastly increased amount of information available on the internet from sources like blogs and social networks, it presents many challenging possibilities for designing new apps. For instance, by employing SA, recommendations made by an algorithm for suggestions may be predicted by considering elements like positive or negative feedback about the items in question [ 8 ]. Sentiment analysis in NLP is crucial for understanding human emotions in text. Its applications include customer feedback analysis, social media monitoring, brand reputation management, political opinion gauging, and healthcare sentiment analysis. Internally, organizations use it for employee feedback assessment. In technology, sentiment analysis enhances user experiences in chatbots and virtual assistants by tailoring responses based on user emotions [ 7 ]. Overall, it plays a pivotal role in extracting subjective information from text across diverse domains. Quantum software engineering is a field that focuses on creating software for quantum computers, which use quantum mechanics principles like superposition and entanglement [ 9 , 10 ]. The field aims to optimize algorithms, exploit quantum systems' parallelism, and solve complex problems efficiently. Key objectives include mitigating errors, developing error correction techniques, and utilizing quantum architectures' computational power. However, quantum software engineering develops software for quantum computers, which use quantum physics to process data. Quantum computing may tackle issues that classical computers cannot, advancing cryptography, optimization, and material science [ 11 , 12 ]. Soft computing is used in quantum software engineering to solve quantum computing problems. Optimizing quantum algorithms, handling imprecise or noisy quantum data, enhancing quantum computing system efficiency and robustness, and inventing new quantum machine learning approaches may be problems[ 13 , 14 ]. Quantum software engineers can improve performance, reliability, and scalability by incorporating soft computing approaches [ 15 ]. This integration may improve quantum algorithms, error correction, and quantum system simulation and modeling, enhancing quantum computing technologies [ 16 , 17 ]. Lexicon-based techniques are frequently used to assess text sentiment since they are simpler and faster than supervised learning approaches [ 18 ]. TextBlob, VADER, and well-known supervised learning classification methods are implemented for this purpose. VADER, a familiar algorithm, is the most efficient and beats other algorithms on social media data, particularly Twitter [ 19 ]. When computing sentiment scores, it also considers emojis. However, while identifying sentiment, TextBlob's method does not take emojis for the sentiment [ 20 ]. The drawback of lexicon-based algorithms for sentiment analysis is that they can't understand symbolic words like sarcasm and irony. When analyzing text that comprises terms with numerous meanings or words used in a nonliteral sense, these algorithms' reliance on pre-defined lists of words and their corresponding sentiment ratings might provide inaccurate results. These algorithms often have trouble using the text's context and tone, resulting in improper sentiment labeling. To overcome these limitations, we have applied supervised learning classification methods. First, we apply lexicon-based methods for sentiment. By seeing restrictions in these methods, we switch to supervised learning classification algorithms for better performance. We created a mechanism to categorize opinions by their positive or negative attitudes from different tweet data [ 21 ]. The primary goals of our research are as follows: We use the Lexicon-based methods and supervised learning classification methods to determine user reviews for quantum computing technology used. We create our dataset with a Twitter application to train the algorithm effectively. Our proposed models have the ability to classify the positive and negative tweets at the base of given tweets. We obtain the positive significant outcomes from the systems to claim the positive used of quantum computing technology. The remainder of the paper is structured as follows. Part 2 demonstrates the literature studies surrounding sentiment analysis. Part 3 describes the design and methodology of our system. Part 4 includes our testing results and a discussion of them. Finally, in Part 5, we outline our research's conclusion and future work. 2. Related Work This section comprehensively reviews previous research and efforts in sentiment analysis techniques. By examining a body of knowledge collected over time, we want to obtain valuable insights, contextual comprehension, and a clear perspective on the evolution of sentiment analysis. Sentiment Analysis Sentiment analysis is a method that uses NLP to automatically extract views, opinions, viewpoints, and feelings from text, voice, tweets, and database sources. Opinions in a text are labeled as "positive," "negative," or "neutral" in sentiment analysis. It is also known as a subjective analysis, opinion mining, or evaluation extraction [ 22 ]. Machine learning techniques that can automatically categorize sentiment have been suggested as one of several strategies for assessing text sentiment. Both supervised and unsupervised learning techniques are effective in this respect [ 23 ]. Large language models can capture context, sarcasm, and other details in sentiment expression, unlike typical lexicon-based methods that depend on specified sentiment lexicons. [ 24 ] investigated the performance of supervised and unsupervised machine learning approaches for sentiment analysis. Their results demonstrated that supervised techniques outperform unsupervised methods, such as lexicon-based algorithms, in terms of accuracy. However, obtaining adequate labeled training data for supervised techniques may be expensive and time-consuming. Recently, a large amount of study has been performed in the area of "Sentiment Analysis on Twitter” by a number of experts. Its original application was in the context of binary classification, in which positive and negative categories receive identical ratings [ 25 ]. Pak and Paroubek [ 26 ] showed a way to say whether a tweet is neutral, positive, or negative. They generated a Twitter collection using the Twitter API to gather tweets and images to name them automatically. Using this information, they made a mood generator based on the multinomial Naive Bayes method. This method uses traits like Ngram and POS tags to predict a person's mood. Their training set also didn't work because it only had tweets with symbols. [ 27 ] utilized the Naive Bayes bigram model and the Maximum Entropy model to determine the topics of tweets. They found that the Maximum Entropy model did not perform as well as the Naive Bayes model. [ 28 ] proposed a method for sentiment analysis for Twitter data based on distant supervision, with their training set consisting of tweets with emoticons acting as noisy labels. They build models using techniques like Naive Bayes, MaxEnt, and Support Vector Machines (SVM). In their feature space, they had POS, bigrams, and unigrams. They discovered that SVM outperformed competing models and that unigrams performed better as features. [ 29 ] developed a two-stage algorithmic process for analyzing the tone of tweets. First, they determined if a tweet was objective or subjective, and then they rated the quality of the emotional tweets. Combining characteristics like the past orientation of words and POS, the feature space utilized includes shares, hashtags, links, punctuation, and exclamation marks [ 30 ]. Bifet and Frank [ 31 ] utilized real-time information from Twitter's Firehouse API, which provided access to every user's public tweets. The Hoeffding tree, stochastic gradient descent, and multinomial naive Bayes were all put through their paces. They concluded that a moderate learning rate was best for the SGD-based model. [ 32 ] developed a paradigm that separates emotional states into positive, negative, and neutral states. Experiments have been conducted using models such as the unigram, feature-based, and tree kernel-based models. They used a "tree kernel," a tree structure, to represent Twitter. The feature-based model only uses 100 traits, unlike the unigram model, around 10,000. They concluded that characteristics such as the words' past orientation and parts-of-speech (pos) identities are the most critical and relevant in the categorizing process. In a three-way competition, the tree kernel-based model won. To use Twitter user-defined hashtags in tweets as a classification of emotion type, [ 33 ] provided a method that makes use of punctuation, single words, n-grams, and patterns as multiple feature types, which are then combined into a unique feature vector for sentiment classification. The K-Nearest Neighbor method was used to assign mood labels to feature vectors constructed for each occurrence in the training and test sets. Liang and Dai [ 34 ] gathered Twitter data using the Twitter API. Three categories of training data are used: camera, video, and mobile. Positive, negative, and non-opinion labels are used to categorize the content. Views on tweets were suppressed. The Unigram Naive Bayes model and the Naive Bayes simplified independence assumption were applied. They used a feature elimination method based on Mutual Information and Chi-Square to get rid of elements that weren't necessary. The course of a tweet can now be anticipated. Superior or inferior, etc. [ 35 ] English tweet orientation may be identified using variants of the Naive Bayes model that have been presented. There are two Naive Bayes classifiers: Baseline (trained to categorize tweets as positive, negative, or neutral) and Binary (classified as positive or negative using a polarity language). Neutral tweets are excluded from consideration. Along with Valence Shifters, Multiword from Multiple Sources, and Polarity Lexicons, classifiers looked at lemmas (nouns, verbs, adjectives, and adverbs). [ 36 ] used the bag-of-words method to determine how people felt. In this method, the connections between words are not considered, and a text is seen as a group of words [ 30 ]. To establish the mood for the whole text, the attitudes of each word were recognized, and their values were merged using different aggregation methods. [ 37 ] utilized the linguistic database WordNet to find a word's emotional meaning across many dimensions. They built a WordNet distance measure and determined the meaning orientation of words. Xia et al., [ 38 ] used an ensemble architecture, combining different feature sets and classification methods, to classify how people felt about something. They used three fundamental models: Naive Bayes, Maximum Entropy, and Support Vector Machines, as well as two types of feature sets: part-of-speech information and word relations. They employed ensemble methods for mood classification, such as fixed combination, weighted combination, and meta-classifier combination, and noticed an improvement in accuracy. [ 39 , 40 ] highlighted the challenges and a helpful technique to get opinions from tweets. Opinion elicitation on Twitter is challenging due to spam and the use of languages that vary greatly. The general model for sentiment analysis is shown in Fig. 1 . The following are steps needed for mood analysis of Twitter data, To the best of our understanding, our work is the first study that performed a detailed analysis of positive and negative comments on quantum computing technology. We add to the literature by giving a snapshot of the early public reactions to this latest technology. 3. Method and Material As seen in Fig. 3 , the research is divided into many phases. The initial step is to gather data via the Twitter API. Once many tweets have been collected, they are all stored in a "comma separated value (csv)" file. In the second stage, preparatory techniques such as case folding, cleaning, word normalization, and stemming were used to increase the classification accuracy. For Lexicon-based Machine Learning Techniques, these strategies are used. TextBlob and VADER Sentiment have been employed in the Lexicon-based approaches to determine each Twitter user's sentiment. We also apply supervised learning categorization models to the collected Tweets for improved performance. The hyper parameter setting for each technique are given in Table 1 . Table 1 Parameter settings for each approach Parameter TextBlob VADER Navie Bayes Batch Size 16 16 32 Optimizer SGD SGD SGD Epochs 60 60 100 Training Phases Two Phases Two Phases Two Phases Phase 1: Learning Rate Range 2e − 2 to 0.0008 3e − 2 to 0.0006 1e − 2 to 0.0005 Phase 2: Learning Rate Range 0.0003 to 0.9991 0.0004 to0.9985 0.0002 to 0.9993 Model Design: Stream Layers 4 layers 4 layers 5 layers Model Design: Batch Normalization Applied Applied Applied Model Design: Global Average Pooling Applied Applied Applied Model Design: SoftMax Classifier Applied Applied Applied Loss Function Cross-Entropy Cross-Entropy Cross-Entropy 3.1. Dataset Python was used as a programming language to gather data from Twitter in real-time. Data were collected between December 1, 2022, and May 31, 2023. Twitter API (Tweepy) was used for the dataset dissemination and collection. An API gathers real-time information from Twitter on the geographic locations of all nations. The application must be registered on Twitter and have a working Twitter account to obtain the tweets. When a user asks the Twitter API for data, the API replies with results that match the user's question. 12538 tweets were used to create a sample. Only good and bad tweets that included the term "#Quantum" were collected for this study. The information that was gathered from the tweets contained user names, screen names, locations, descriptions, fans, and following numbers. 3.2. Data Preprocessing Text mining requires a main section, essentially getting ready to transform the document and give it more structure. The following preprocessing processes are used in this analysis: Case folding is the initial stage that changes complete text into lowercase, i.e., “Quantum! Impressive in Different Fields of Software Engineering” into “quantum! impressive in different fields of software engineering” Data Cleaning : Cleansing is necessary to generate the numbers utilized in this study. We filter out client-specific terminology, symbols, abbreviations, and tweets at this stage. Only words made up of letters and digits are left at this stage. Stop-words removal : Stop-words are words in language that can be safely removed without compromising sentence meaning. Researchers often remove function words like "the", "at", and "which" from stop-words. Text normalization : Text normalization reduces word variations to their common root form, simplifying modeling and improving model performance. Lemmatization : Lemmatization is the process of mapping words to their base form using vocabulary and morphological analysis, such as converting words like "sang" and "sung" to "sing". Stemming : Stemming is the step-by-step process of changing words in a text into their primary forms following set criteria. Data distribution : Data distribution for testing and training. As indicated in Fig. 2 , for the experiment, we utilized 70% of the data for training and 30% for testing. 3.3. Lexicon-Based Approaches Lexicon-based or rule-based approaches are a class of methods used in natural language processing and sentiment analysis to determine the sentiment or polarity of a piece of text based on predefined word lists or lexicons [ 41 ]. These approaches rely on sentiment lexicons, dictionaries containing words, and their associated sentiment scores or polarities (e.g., positive, negative). We employed the two algorithms TextBlob and VADER (Valence Aware Dictionary for Sentiment Reasoning), to assess text polarity and conduct a semantic analysis. 3.3.1. TextBlob : TextBlob is a Python tool (much like a Python string) for processing written data. It seeks to provide a unified API to handle standard NLP operations, such as part-of-speech tagging, word phrase extraction, translation, text mining, text processing modules, text analysis, sentiment analysis, classification, and more. The text is reviewed at the phrase level by TextBlob [ 42 , 43 ]. It begins by extracting data from the dataset and divides the review into words. The direction of the entire dataset can be determined by counting the amount of negative and positive comments and deciding whether the answer is positive or negative based on the sum of negative and positive reviews. Use the sentiment () tool to determine the polarity and subjectivity of a particular review. Polarity and subjectivity are the two parameters that make up the set of values provided. The polarity score ranges from − 1 to 1, and the function produces a tuple with polarity and subjectivity. The range of subjectivity is 0 to 1, with 0 being the most objective and 1 being the least. 3.3.2 VADER : Specifically used for sentiment analysis, is a rule-based lexicon and analysis method. It is used to extract feelings stated in social media and performs remarkably well in this regard. The fundamental basis of VADER sentiment analysis [ 43 ] is a set of essential elements, including capitalization, conjunctions, degree modifiers, punctuation, and preceding trigrams. VADER categorizes behaviors into positive and negative categories and gives complicated scores, which are determined by adding each word's valence scores in the vocabulary and standardizing them in the range (-1, 1), with "1" being the most extreme positive and "-1" being the most potent negative. If the combined score is less than − 0.05, the text will be considered negative; if the score is higher than 0.05, the text will be considered positive. One significant advantage of VADER is that it can generate sentiment polarity straight from the raw tweets without data processing. Additionally, it supports emoji for identifying emotions and is quick enough to be utilized online without degrading speed performance. 3.3. Sentiment Labelling Stage After preprocessing is done, the technique of sentiment labeling involves the use of an opinion lexicon. A collection of words or phrases classified according to the polarity of the attitude, such as positive or negative, makes up an opinion lexicon. An opinion lexicon is frequently used in sentiment analysis to aid in interpreting and extracting opinion polarity or sentiment from the text by computers or computer programs. 3.4. Naive Bayes Classifier The Naive Bayes classification model's implementation stage is the following step: After the tweet data has been labeled. This phase will evaluate the proposed model's efficacy in detecting positive and negative sentiments concerning quantum computing technology among Twitter users. Nave Bayes offers a fresh approach to assessing and comprehending data and the capacity to run models quickly and make forecasts [ 44 ]. Because of the vast amount of Twitter data, it is necessary to develop the classification model further. F-score is a statistical method that can be used to measure the performance of a model or program where accuracy and recall value at output then find the F-score. The learning subset teaches the model or algorithm, and the F-score verifies it [ 44 ]. Below is a flowchart detailing the steps required to implement the Naive Bayes classification model, TextBlob, and VADER. Figure 3 shows the overall architecture of our proposed network. 4. Results and Discussion Sentiment analysis is carried out on Twitter social media user reviews. The tweets used as the study's data source cover six months of quantum computing beginning. Tweets are analyzed using TextBlob, VADER, and Naive Bayes classification. According to the research findings, most quantum users are pleased with their experience with this technology. However, the sentiment research revealed that a tiny percentage of users indicated that quantum computing technology gave erroneous-false results and disagreed with the result. Although most tweets are associated with happy feelings, there are also tweets related to anger, anticipation, contempt, fear, and sadness. Furthermore, when the emotional strength of the most often used terms in tweets is studied, positive and acceptable feelings such as excellent, perfect, fantastic, amazed, and like are usually featured. However, the same study noted harmful emotional levels such as bad, scared, dangerous, and wrong, albeit with relatively less intensity. Discussion on the results is covered in the following sub-sections. 4.1 TextBlob Analysis The TextBlob technique is used to analyze sentiment. The TextBlob Python NLTK module classifies text data according to positive and negative emotions. It is frequently used for language translation, emotion analysis, and speech recognition. TextBlob can carry out complex operations on text data, while NLTK provides simple access to millions of linguistic resources [ 45 ]. TextBlob gives the Polarity and subjectivity of a given text. Polarity is between [-1 and 1], where − 1 shows the negative sentiment and 1 indicates the positive sentiment. When we apply TextBlob to all collected tweets, it classifies the positive and negative tweets. After classifying, we obtain the different ratios in positive and negative tweets. Figure 4 (a) demonstrates all tweets, Fig. 4 (b) shows the positive and negative tweets, and Fig. 4 (c) explains the ratio between positive and negative tweets. It is clear from Fig. 4 (c) the positive tweets are 78%, and the negative tweets have only 22% that show users have positive reviews about quantum. 6.2 VADER Analysis This section discusses the findings of a sentiment analysis conducted on Twitter using VADER's technologies. According to the VADER sentiment Analyzer, Fig. 5 displays the sentiment score for each tweet as either positive or negative. After the limitations were applied, the categorization of the tweets as positive and negative is shown in Fig. 5 (b). Figure 5 (b) demonstrates how we might classify tweets directly as positive or negative using VADER if we selected the proper threshold value. However, curiously, as seen in Fig. 5 (c), 81% of the tweets conveyed favorable thoughts, while just 19% said the opposite. Comparing positive and negative tweets, the positive proportion is higher. Because the tiny number of tweets produced imbalanced data and the belief that the threshold value may provide a significant number of neutral viewpoints among all other classes. The results of this research point to the potential application of the VADER Sentiment Analysis to evaluate feelings expressed in tweets and categorize them appropriately, generating accurate results. 6.3 Naive Bayes Analysis This research used three classifiers: Naive Bayes, Rocchio, and Perceptron, to assess and contrast their performance in predicting sentiment in Twitter comments related to quantum. A dataset of 12,538 tweets from diverse users was gathered, with each tweet labeled as positive or negative to ensure accuracy and reliability. Table 2 displays status updates within each class, providing a sample of sentiments encountered during the study. The study aimed to reveal the comparative strengths and weaknesses of Naive Bayes, Rocchio, and Perceptron classifiers, allowing insights into their efficacy in sentiment analysis for quantum-related content on Twitter. Table 2 Select tweets from random users for labeling. Tweets Sentiment Analysis Quantum! Impressive in different fields of software engineering! #Quantum 🌀 Positive #Quantum computing's energy demands raise environmental worries, urging the need for sustainable solutions. #EnergyConsumption 🔋 Negative The race for #Quantum advantage signals a promising future, where complex problems will find innovative solutions at lightning speed. ⚡ Positive #Quantum computing: the key to solving complex problems at an unimaginable speed. #GameChanger 🖥️ Positive Quantum computing's potential disruptions in data security raise concerns about privacy and encryption vulnerabilities. #SecurityRisk 🔒 Negative Prepare for a quantum-powered future that promises unrivaled technological advancements! #Quantum ⚡ Positive The era of #Quantum supremacy is upon us, promising unparalleled computational power and transformative technological advancements. 💡 Positive The study divided the dataset into multiple partitions and used consistent training and testing sets across all classifiers. Performance evaluations were based on three metrics: precision, recall, and F-score. The capacity of a classifier to identify positive examples accurately is measured by precision. In contrast, the ability to identify all positive instances, including those that are predicted and missed, is calculated by a recall. When evaluating the classifier's performance, the F-score, a harmonic mean of precision and recall, considers both erroneous positives and false negatives. The comparative results of the classifiers are presented in tables, providing valuable insights into their performance in terms of precision, recall, and F-score. Analyzing these metrics can help conclude the effectiveness and robustness of the different classifiers used in the study. The calculations presented below have been utilized to compare the classifiers based on the precision, recall, and F-score performance measures. The Precision and Recall performance of Nave Bayes, Rocchio, and Perceptron are displayed in Tables 3 , 4 and 5 , respectively. $$\text{P}\text{r}\text{e}\text{c}\text{i}\text{s}\text{i}\text{o}\text{n}=\frac{\text{T}\text{P}}{\text{T}\text{P}+\text{F}\text{P}}$$ 1 $$\text{R}\text{e}\text{c}\text{a}\text{l}\text{l}=\frac{\text{T}\text{P}}{\text{T}\text{P}+\text{F}\text{N}}$$ 2 $$\text{F}-\text{s}\text{c}\text{o}\text{r}\text{e}=\frac{2\text{*}\text{P}\text{r}\text{e}\text{c}\text{i}\text{s}\text{i}\text{o}\text{n}\text{*}\text{R}\text{e}\text{c}\text{a}\text{l}\text{l}}{\text{P}\text{r}\text{e}\text{c}\text{i}\text{s}\text{i}\text{o}\text{n}+\text{R}\text{e}\text{c}\text{a}\text{l}\text{l}}$$ 3 Equ. 1 and Equ.2 are used for finding the Precision and Recall value, while Equ.3 calculating the F-score. Table 3 Naive Bayes Precision and Recall Performance. Naive Bayes Classifier Actual Positive Actual Negative Predicted Positive 0.93 0.07 Predicted Negative 0.15 0.85 Table 4 Rocchio Precision and Recall Performance. Rocchio Classifier Actual Positive Actual Negative Predicted Positive 0.91 0.09 Predicted Negative 0.14 0.86 Table 5 Perceptron Precision and Recall Performance. Perceptron Classifier Actual Positive Actual Negative Predicted Positive 0.88 0.12 Predicted Negative 0.19 0.81 The results of each classifier are summarized in Table 6 , which compares their levels of Precision, Recall, and F-score. Table 6 Precision, Recall, and F-score comparison of the three classifiers. Naive Bayes Classifier Rocchio Classifier Perceptron Classifier Precision 0.93 0.91 0.88 Recall 0.86 0.85 0.82 F-score 0.88 0.87 0.84 The F-score reveals that the Naive Bayes classifier has the highest performance, whereas the perceptron classifier has the lowest. Although Rocchio's performance was almost comparable to that of Naive Bayes. 5. Conclusion In this paper, we have examined the use of quantum computing technology via sentiment analysis. Sentiment analysis has become increasingly important in understanding customer feedback and opinions in today’s digital world. Lexicon-based methods and supervised learning algorithms have been popularly used for this task. They require human-annotated text to train the classifier, while lexicon-based methods have recently been used for this task. This study evaluated sentiment analysis by labeling a distinct dataset, focusing on examining the positive impact of quantum innovation. It outperforms supervised learning classifiers and lexicon-based methods in a given dataset. Based on the findings, it is possible to draw the conclusion that Twitter users generally have favorable opinions towards the use of quantum computing technology. It would be beneficial to elaborate on how this increased speed may revolutionize various industries, enhance computing capabilities, or potentially introduce new applications for future implications. Moreover, discussing potential challenges, ethical considerations, or public concerns surrounding quantum technology would provide a more comprehensive response. In the future, we will use social media to interpret different types of innovative quantum-based technology to analyze present soft computing quantum software engineering technology impact. Declarations Author Contributions: Adel Assiri and Abdu Gumaei do methodology and formal analysis; Faisal Mehmood does data collection; and Sami Ullah does the final revision and English polishing. All authors have read and agreed to the published version of the manuscript. 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Knowl Inf Syst 60:617–663 Bordoloi M, Biswas SK (2023) Sentiment analysis: A survey on design framework, applications and future scopes, Artificial Intelligence Review, pp. 1–56 Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining, in LREc, 2010, vol. 10, no. pp. 1320–1326 Parikh R, Movassate M (2009) Sentiment analysis of user-generated twitter updates using various classification techniques, CS224N final report, vol. 118, pp. 1–18 Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision, CS224N project report, Stanford, vol. 1, no. 12, p. 2009 Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data, in Coling 2010. Posters, pp 36–44 Mehmood F, Chen E, Akbar MA, Alsanad AA (2021) Human action recognition of spatiotemporal parameters for skeleton sequences using MTLN feature learning framework, Electronics. 10(21):2708 Bifet A, Frank E (2010) Sentiment knowledge discovery in twitter streaming data, in International conference on discovery science, : Springer, pp. 1–15 Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau RJ (2011) Sentiment analysis of twitter data, in Proceedings of the workshop on language in social media (LSM 2011), pp. 30–38 Davidov D, Tsur O, Rappoport A (2010) Enhanced sentiment learning using twitter hashtags and smileys, in Coling 2010. Posters, pp 241–249 Liang P-W, Dai B-R (2013) Opinion mining on social media data, in IEEE 14th international conference on mobile data management, 2013, vol. 2: IEEE, pp. 91–96 Gamallo P, Garcia M (2014) Citius: A naive-bayes strategy for sentiment analysis on english tweets, in Proceedings of the 8th international Workshop on Semantic Evaluation (SemEval 2014), pp. 171–175 Farnaaz SK, Sureshbabu A (2022) Twitter Sentiment Anal Using Deep Learn Techniques Othman R, Abdelsadek Y, Chelghoum K, Kacem I, Faiz R (2019) Improving sentiment analysis in twitter using sentiment specific word embeddings, in 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), vol. 2: IEEE, pp. 854–858 Xia R, Zong C, Li S (2011) Ensemble of feature sets and classification algorithms for sentiment classification, Information sciences, vol. 181, no. 6, pp. 1138–1152 Luo Z, Osborne M, Wang T (2015) An effective approach to tweets opinion retrieval. World Wide Web 18:545–566 Mehmood F, Chen E, Abbas T, Akbar MA, Khan AA (2023) Automatically human action recognition (HAR) with view variation from skeleton means of adaptive transformer network, Soft Computing, pp. 1–20 Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis, Computational linguistics, vol. 37, no. 2, pp. 267–307 Razzaq A et al (2022) Extraction of psychological effects of COVID-19 pandemic through topic-level sentiment dynamics, Complexity, vol. 2022 Endsuy RD (2021) Sentiment analysis between VADER and EDA for the US presidential election 2020 on twitter datasets. J Appl Data Sci 2(1):08–18 Erfina A, Nurul MR (2023) Implementation of Naive Bayes classification algorithm for Twitter user sentiment analysis on ChatGPT using Python programming language, Data & Metadata, vol. 2, pp. 45–45 Bonta V, Kumaresh N, Janardhan N (2019) A comprehensive study on lexicon based approaches for sentiment analysis. Asian J Comput Sci Technol 8(S2):1–6 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3999636","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275726009,"identity":"78b139d1-29b9-4cca-bdf1-464691ceaf03","order_by":0,"name":"Adel Assiri","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Adel","middleName":"","lastName":"Assiri","suffix":""},{"id":275726010,"identity":"1b443f91-e390-490c-9f03-52e8b3bbe335","order_by":1,"name":"Abdu Gumaei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBADHn4og7GBaC2SDaRqYTA4QKwW/gb2a1I3amxkjG+3P/zMw2Aju+EA+8MP+LRIHOApk845lsZjdueMsTQPQ5rxhgM8xhJ4rTnAkyadw3aYx+xGDgNQy+FEoBYGvFrkwVr+/ecxnpH++DcPw3+gFvbHP/BpMTjAfkw6t+0Aj4FEghnQlgNALQxmeG0xPMzDbJ3bl8wjceeMmeUcg2TjmUBHWuDTIne8/eHtnG929vyz2x/feFNhJ9t3HMjAp4WBmccAwgA7BsRmxqseBNgfIGkZBaNgFIyCUYAFAAAOKkYfbD3jAwAAAABJRU5ErkJggg==","orcid":"","institution":"Prince Sattam bin Abdulaziz University","correspondingAuthor":true,"prefix":"","firstName":"Abdu","middleName":"","lastName":"Gumaei","suffix":""},{"id":275726011,"identity":"083b3c51-ce5a-45d5-a2b9-c8021078a094","order_by":2,"name":"Faisal Mehmood","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Faisal","middleName":"","lastName":"Mehmood","suffix":""},{"id":275726012,"identity":"10f91a5a-5844-4522-9134-70da8adedab5","order_by":3,"name":"Sami Ullah","email":"","orcid":"","institution":"Government College University Faisalabad","correspondingAuthor":false,"prefix":"","firstName":"Sami","middleName":"","lastName":"Ullah","suffix":""}],"badges":[],"createdAt":"2024-02-29 12:31:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3999636/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3999636/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52046421,"identity":"2f51d69b-2037-4ec2-9256-7c07eeccb3fe","added_by":"auto","created_at":"2024-03-05 20:08:25","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39946,"visible":true,"origin":"","legend":"\u003cp\u003eSentiment Analysis Architecture.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3999636/v1/893ec4fa67d02cb44b8e61c0.jpeg"},{"id":52046423,"identity":"f4201b2e-eaec-4095-8402-f4d7bbf65c73","added_by":"auto","created_at":"2024-03-05 20:08:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2744,"visible":true,"origin":"","legend":"\u003cp\u003eData Distribution\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3999636/v1/a4825bd92a6ed865f4b6bb0b.png"},{"id":52046422,"identity":"109ad4aa-c02e-47ef-a106-09d4329787e2","added_by":"auto","created_at":"2024-03-05 20:08:25","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87737,"visible":true,"origin":"","legend":"\u003cp\u003eProposed network architecture.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3999636/v1/59f007bd5ee3d602bed1273d.jpeg"},{"id":52046424,"identity":"a48919e4-2795-4e70-8fac-173de6d3baeb","added_by":"auto","created_at":"2024-03-05 20:08:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":373988,"visible":true,"origin":"","legend":"\u003cp\u003ePositive and negative tweets classification results from TextBlob.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3999636/v1/92a2b3a2c457866684823ade.png"},{"id":52046425,"identity":"54b4e844-8c25-4480-b9cd-efdbe349ba96","added_by":"auto","created_at":"2024-03-05 20:08:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":387351,"visible":true,"origin":"","legend":"\u003cp\u003ePositive and negative tweets classification results from VADER.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3999636/v1/78653e721bb5b835d2cdc97e.png"},{"id":52092265,"identity":"955e966d-7bfc-4c39-ae88-d2676b7c443b","added_by":"auto","created_at":"2024-03-06 14:24:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1218741,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3999636/v1/f131bdbd-f858-4d35-a623-c3dd631297e9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social Media User Evaluation for Quantum Computing Technology Via Sentiment Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eQuantum computing leverages principles like superposition and entanglement from quantum mechanics to process information in quantum bits (qubits) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Superposition enables qubits to exist in multiple states simultaneously, exponentially increasing computational possibilities. Entanglement links qubits, allowing them to be correlated, even at a distance. This unique capability enhances computational power. Quantum computing's potential lies in revolutionizing fields such as cryptography, where it can break current encryption methods, optimization by solving complex problems more efficiently, and material science for simulating molecular structures and properties at a profound level [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, individuals express their thoughts and opinions differently through the internet. Nowadays, it is usually done through blog posts, internet forums, product review websites, social media, etc. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Millions of people today use social networking sites such as Facebook, Twitter, Google Plus, and others to share their ideas and opinions about everyday life. The interactive media offered by social networking sites allows users to use forums to educate and persuade others. Sentiment analysis data is abundant in tweets, status updates, blog posts, comments, reviews, and other social media material. In addition, social media allows companies to reach the people they want and promote their products or services [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. People heavily rely on online user-generated content when making decisions. For instance, before deciding to purchase a product or pay for a service, people often read about it online and discuss it via social media. There is not enough user-generated content for each user to process. Various sentiment analysis techniques are in use because the process must be automated [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSentiment analysis (SA) helps users to access product information's satisfaction level before purchasing. Marketers and organizations use analytical data to get information about their goods or services, enabling them to tailor their offerings to meet users' needs and preferences [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The primary goals of textual information retrieval techniques are processing, looking for, or examining the material already available. Although there is an objective component to the facts, other aspects of literature are inherently subjective. SA, which studies views, feelings, assessments, attitudes, and emotions, depends mainly on these contents. Due to the vastly increased amount of information available on the internet from sources like blogs and social networks, it presents many challenging possibilities for designing new apps. For instance, by employing SA, recommendations made by an algorithm for suggestions may be predicted by considering elements like positive or negative feedback about the items in question [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Sentiment analysis in NLP is crucial for understanding human emotions in text. Its applications include customer feedback analysis, social media monitoring, brand reputation management, political opinion gauging, and healthcare sentiment analysis. Internally, organizations use it for employee feedback assessment. In technology, sentiment analysis enhances user experiences in chatbots and virtual assistants by tailoring responses based on user emotions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Overall, it plays a pivotal role in extracting subjective information from text across diverse domains.\u003c/p\u003e \u003cp\u003eQuantum software engineering is a field that focuses on creating software for quantum computers, which use quantum mechanics principles like superposition and entanglement [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The field aims to optimize algorithms, exploit quantum systems' parallelism, and solve complex problems efficiently. Key objectives include mitigating errors, developing error correction techniques, and utilizing quantum architectures' computational power. However, quantum software engineering develops software for quantum computers, which use quantum physics to process data. Quantum computing may tackle issues that classical computers cannot, advancing cryptography, optimization, and material science [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Soft computing is used in quantum software engineering to solve quantum computing problems. Optimizing quantum algorithms, handling imprecise or noisy quantum data, enhancing quantum computing system efficiency and robustness, and inventing new quantum machine learning approaches may be problems[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Quantum software engineers can improve performance, reliability, and scalability by incorporating soft computing approaches [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This integration may improve quantum algorithms, error correction, and quantum system simulation and modeling, enhancing quantum computing technologies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLexicon-based techniques are frequently used to assess text sentiment since they are simpler and faster than supervised learning approaches [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. TextBlob, VADER, and well-known supervised learning classification methods are implemented for this purpose. VADER, a familiar algorithm, is the most efficient and beats other algorithms on social media data, particularly Twitter [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. When computing sentiment scores, it also considers emojis. However, while identifying sentiment, TextBlob's method does not take emojis for the sentiment [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The drawback of lexicon-based algorithms for sentiment analysis is that they can't understand symbolic words like sarcasm and irony. When analyzing text that comprises terms with numerous meanings or words used in a nonliteral sense, these algorithms' reliance on pre-defined lists of words and their corresponding sentiment ratings might provide inaccurate results. These algorithms often have trouble using the text's context and tone, resulting in improper sentiment labeling. To overcome these limitations, we have applied supervised learning classification methods. First, we apply lexicon-based methods for sentiment. By seeing restrictions in these methods, we switch to supervised learning classification algorithms for better performance. We created a mechanism to categorize opinions by their positive or negative attitudes from different tweet data [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe primary goals of our research are as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWe use the Lexicon-based methods and supervised learning classification methods to determine user reviews for quantum computing technology used.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWe create our dataset with a Twitter application to train the algorithm effectively.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOur proposed models have the ability to classify the positive and negative tweets at the base of given tweets.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWe obtain the positive significant outcomes from the systems to claim the positive used of quantum computing technology.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe remainder of the paper is structured as follows. Part 2 demonstrates the literature studies surrounding sentiment analysis. Part 3 describes the design and methodology of our system. Part 4 includes our testing results and a discussion of them. Finally, in Part 5, we outline our research's conclusion and future work.\u003c/p\u003e"},{"header":"2. Related Work","content":"\u003cp\u003eThis section comprehensively reviews previous research and efforts in sentiment analysis techniques. By examining a body of knowledge collected over time, we want to obtain valuable insights, contextual comprehension, and a clear perspective on the evolution of sentiment analysis.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSentiment Analysis\u003c/strong\u003e \u003cp\u003eSentiment analysis is a method that uses NLP to automatically extract views, opinions, viewpoints, and feelings from text, voice, tweets, and database sources. Opinions in a text are labeled as \"positive,\" \"negative,\" or \"neutral\" in sentiment analysis. It is also known as a subjective analysis, opinion mining, or evaluation extraction [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Machine learning techniques that can automatically categorize sentiment have been suggested as one of several strategies for assessing text sentiment. Both supervised and unsupervised learning techniques are effective in this respect [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Large language models can capture context, sarcasm, and other details in sentiment expression, unlike typical lexicon-based methods that depend on specified sentiment lexicons. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] investigated the performance of supervised and unsupervised machine learning approaches for sentiment analysis. Their results demonstrated that supervised techniques outperform unsupervised methods, such as lexicon-based algorithms, in terms of accuracy. However, obtaining adequate labeled training data for supervised techniques may be expensive and time-consuming. Recently, a large amount of study has been performed in the area of \"Sentiment Analysis on Twitter\u0026rdquo; by a number of experts. Its original application was in the context of binary classification, in which positive and negative categories receive identical ratings [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePak and Paroubek [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] showed a way to say whether a tweet is neutral, positive, or negative. They generated a Twitter collection using the Twitter API to gather tweets and images to name them automatically. Using this information, they made a mood generator based on the multinomial Naive Bayes method. This method uses traits like Ngram and POS tags to predict a person's mood. Their training set also didn't work because it only had tweets with symbols. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] utilized the Naive Bayes bigram model and the Maximum Entropy model to determine the topics of tweets. They found that the Maximum Entropy model did not perform as well as the Naive Bayes model. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] proposed a method for sentiment analysis for Twitter data based on distant supervision, with their training set consisting of tweets with emoticons acting as noisy labels. They build models using techniques like Naive Bayes, MaxEnt, and Support Vector Machines (SVM). In their feature space, they had POS, bigrams, and unigrams. They discovered that SVM outperformed competing models and that unigrams performed better as features. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] developed a two-stage algorithmic process for analyzing the tone of tweets. First, they determined if a tweet was objective or subjective, and then they rated the quality of the emotional tweets. Combining characteristics like the past orientation of words and POS, the feature space utilized includes shares, hashtags, links, punctuation, and exclamation marks [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBifet and Frank [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] utilized real-time information from Twitter's Firehouse API, which provided access to every user's public tweets. The Hoeffding tree, stochastic gradient descent, and multinomial naive Bayes were all put through their paces. They concluded that a moderate learning rate was best for the SGD-based model. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] developed a paradigm that separates emotional states into positive, negative, and neutral states. Experiments have been conducted using models such as the unigram, feature-based, and tree kernel-based models. They used a \"tree kernel,\" a tree structure, to represent Twitter. The feature-based model only uses 100 traits, unlike the unigram model, around 10,000. They concluded that characteristics such as the words' past orientation and parts-of-speech (pos) identities are the most critical and relevant in the categorizing process. In a three-way competition, the tree kernel-based model won.\u003c/p\u003e \u003cp\u003eTo use Twitter user-defined hashtags in tweets as a classification of emotion type, [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] provided a method that makes use of punctuation, single words, n-grams, and patterns as multiple feature types, which are then combined into a unique feature vector for sentiment classification. The K-Nearest Neighbor method was used to assign mood labels to feature vectors constructed for each occurrence in the training and test sets.\u003c/p\u003e \u003cp\u003eLiang and Dai [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] gathered Twitter data using the Twitter API. Three categories of training data are used: camera, video, and mobile. Positive, negative, and non-opinion labels are used to categorize the content. Views on tweets were suppressed. The Unigram Naive Bayes model and the Naive Bayes simplified independence assumption were applied. They used a feature elimination method based on Mutual Information and Chi-Square to get rid of elements that weren't necessary. The course of a tweet can now be anticipated. Superior or inferior, etc. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] English tweet orientation may be identified using variants of the Naive Bayes model that have been presented. There are two Naive Bayes classifiers: Baseline (trained to categorize tweets as positive, negative, or neutral) and Binary (classified as positive or negative using a polarity language). Neutral tweets are excluded from consideration. Along with Valence Shifters, Multiword from Multiple Sources, and Polarity Lexicons, classifiers looked at lemmas (nouns, verbs, adjectives, and adverbs). [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] used the bag-of-words method to determine how people felt. In this method, the connections between words are not considered, and a text is seen as a group of words [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To establish the mood for the whole text, the attitudes of each word were recognized, and their values were merged using different aggregation methods. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] utilized the linguistic database WordNet to find a word's emotional meaning across many dimensions. They built a WordNet distance measure and determined the meaning orientation of words.\u003c/p\u003e \u003cp\u003eXia et al., [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] used an ensemble architecture, combining different feature sets and classification methods, to classify how people felt about something. They used three fundamental models: Naive Bayes, Maximum Entropy, and Support Vector Machines, as well as two types of feature sets: part-of-speech information and word relations. They employed ensemble methods for mood classification, such as fixed combination, weighted combination, and meta-classifier combination, and noticed an improvement in accuracy. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] highlighted the challenges and a helpful technique to get opinions from tweets. Opinion elicitation on Twitter is challenging due to spam and the use of languages that vary greatly. The general model for sentiment analysis is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe following are steps needed for mood analysis of Twitter data,\u003c/p\u003e \u003cp\u003eTo the best of our understanding, our work is the first study that performed a detailed analysis of positive and negative comments on quantum computing technology. We add to the literature by giving a snapshot of the early public reactions to this latest technology.\u003c/p\u003e"},{"header":"3. Method and Material","content":"\u003cp\u003eAs seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the research is divided into many phases. The initial step is to gather data via the Twitter API. Once many tweets have been collected, they are all stored in a \"comma separated value (csv)\" file. In the second stage, preparatory techniques such as case folding, cleaning, word normalization, and stemming were used to increase the classification accuracy. For Lexicon-based Machine Learning Techniques, these strategies are used. TextBlob and VADER Sentiment have been employed in the Lexicon-based approaches to determine each Twitter user's sentiment. We also apply supervised learning categorization models to the collected Tweets for improved performance. The hyper parameter setting for each technique are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eParameter settings for each approach\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\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTextBlob\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVADER\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNavie Bayes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatch Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSGD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpochs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining Phases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo Phases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTwo Phases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTwo Phases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhase 1: Learning Rate Range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2e\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e to 0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3e\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e to 0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1e\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e to 0.0005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhase 2: Learning Rate Range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0003 to 0.9991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0004 to0.9985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002 to 0.9993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Design: Stream Layers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 layers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 layers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 layers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Design: Batch Normalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApplied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApplied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApplied\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Design: Global Average Pooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApplied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApplied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApplied\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Design: SoftMax Classifier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApplied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApplied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApplied\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss Function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-Entropy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-Entropy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-Entropy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Dataset\u003c/h2\u003e \u003cp\u003ePython was used as a programming language to gather data from Twitter in real-time. Data were collected between December 1, 2022, and May 31, 2023. Twitter API (Tweepy) was used for the dataset dissemination and collection. An API gathers real-time information from Twitter on the geographic locations of all nations. The application must be registered on Twitter and have a working Twitter account to obtain the tweets. When a user asks the Twitter API for data, the API replies with results that match the user's question. 12538 tweets were used to create a sample. Only good and bad tweets that included the term \"#Quantum\" were collected for this study. The information that was gathered from the tweets contained user names, screen names, locations, descriptions, fans, and following numbers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Data Preprocessing\u003c/h2\u003e \u003cp\u003eText mining requires a main section, essentially getting ready to transform the document and give it more structure. The following preprocessing processes are used in this analysis: Case folding is the initial stage that changes complete text into lowercase, i.e., \u0026ldquo;Quantum! Impressive in Different Fields of Software Engineering\u0026rdquo; into \u0026ldquo;quantum! impressive in different fields of software engineering\u0026rdquo;\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eData Cleaning\u003c/b\u003e: Cleansing is necessary to generate the numbers utilized in this study. We filter out client-specific terminology, symbols, abbreviations, and tweets at this stage. Only words made up of letters and digits are left at this stage.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStop-words removal\u003c/b\u003e: Stop-words are words in language that can be safely removed without compromising sentence meaning. Researchers often remove function words like \"the\", \"at\", and \"which\" from stop-words.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eText normalization\u003c/b\u003e: Text normalization reduces word variations to their common root form, simplifying modeling and improving model performance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLemmatization\u003c/b\u003e: Lemmatization is the process of mapping words to their base form using vocabulary and morphological analysis, such as converting words like \"sang\" and \"sung\" to \"sing\".\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStemming\u003c/b\u003e: Stemming is the step-by-step process of changing words in a text into their primary forms following set criteria.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eData distribution\u003c/b\u003e: Data distribution for testing and training. As indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, for the experiment, we utilized 70% of the data for training and 30% for testing.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Lexicon-Based Approaches\u003c/h2\u003e \u003cp\u003eLexicon-based or rule-based approaches are a class of methods used in natural language processing and sentiment analysis to determine the sentiment or polarity of a piece of text based on predefined word lists or lexicons [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These approaches rely on sentiment lexicons, dictionaries containing words, and their associated sentiment scores or polarities (e.g., positive, negative). We employed the two algorithms TextBlob and VADER (Valence Aware Dictionary for Sentiment Reasoning), to assess text polarity and conduct a semantic analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3.1. TextBlob\u003c/b\u003e: TextBlob is a Python tool (much like a Python string) for processing written data. It seeks to provide a unified API to handle standard NLP operations, such as part-of-speech tagging, word phrase extraction, translation, text mining, text processing modules, text analysis, sentiment analysis, classification, and more. The text is reviewed at the phrase level by TextBlob [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It begins by extracting data from the dataset and divides the review into words. The direction of the entire dataset can be determined by counting the amount of negative and positive comments and deciding whether the answer is positive or negative based on the sum of negative and positive reviews. Use the sentiment () tool to determine the polarity and subjectivity of a particular review. Polarity and subjectivity are the two parameters that make up the set of values provided. The polarity score ranges from \u0026minus;\u0026thinsp;1 to 1, and the function produces a tuple with polarity and subjectivity. The range of subjectivity is 0 to 1, with 0 being the most objective and 1 being the least.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3.2 VADER\u003c/b\u003e: Specifically used for sentiment analysis, is a rule-based lexicon and analysis method. It is used to extract feelings stated in social media and performs remarkably well in this regard. The fundamental basis of VADER sentiment analysis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] is a set of essential elements, including capitalization, conjunctions, degree modifiers, punctuation, and preceding trigrams. VADER categorizes behaviors into positive and negative categories and gives complicated scores, which are determined by adding each word's valence scores in the vocabulary and standardizing them in the range (-1, 1), with \"1\" being the most extreme positive and \"-1\" being the most potent negative. If the combined score is less than \u0026minus;\u0026thinsp;0.05, the text will be considered negative; if the score is higher than 0.05, the text will be considered positive. One significant advantage of VADER is that it can generate sentiment polarity straight from the raw tweets without data processing. Additionally, it supports emoji for identifying emotions and is quick enough to be utilized online without degrading speed performance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Sentiment Labelling Stage\u003c/h2\u003e \u003cp\u003eAfter preprocessing is done, the technique of sentiment labeling involves the use of an opinion lexicon. A collection of words or phrases classified according to the polarity of the attitude, such as positive or negative, makes up an opinion lexicon. An opinion lexicon is frequently used in sentiment analysis to aid in interpreting and extracting opinion polarity or sentiment from the text by computers or computer programs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Naive Bayes Classifier\u003c/h2\u003e \u003cp\u003eThe Naive Bayes classification model's implementation stage is the following step: After the tweet data has been labeled. This phase will evaluate the proposed model's efficacy in detecting positive and negative sentiments concerning quantum computing technology among Twitter users. Nave Bayes offers a fresh approach to assessing and comprehending data and the capacity to run models quickly and make forecasts [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Because of the vast amount of Twitter data, it is necessary to develop the classification model further. F-score is a statistical method that can be used to measure the performance of a model or program where accuracy and recall value at output then find the F-score. The learning subset teaches the model or algorithm, and the F-score verifies it [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBelow is a flowchart detailing the steps required to implement the Naive Bayes classification model, TextBlob, and VADER. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the overall architecture of our proposed network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eSentiment analysis is carried out on Twitter social media user reviews. The tweets used as the study's data source cover six months of quantum computing beginning. Tweets are analyzed using TextBlob, VADER, and Naive Bayes classification. According to the research findings, most quantum users are pleased with their experience with this technology. However, the sentiment research revealed that a tiny percentage of users indicated that quantum computing technology gave erroneous-false results and disagreed with the result. Although most tweets are associated with happy feelings, there are also tweets related to anger, anticipation, contempt, fear, and sadness. Furthermore, when the emotional strength of the most often used terms in tweets is studied, positive and acceptable feelings such as excellent, perfect, fantastic, amazed, and like are usually featured. However, the same study noted harmful emotional levels such as bad, scared, dangerous, and wrong, albeit with relatively less intensity. Discussion on the results is covered in the following sub-sections.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 TextBlob Analysis\u003c/h2\u003e \u003cp\u003eThe TextBlob technique is used to analyze sentiment. The TextBlob Python NLTK module classifies text data according to positive and negative emotions. It is frequently used for language translation, emotion analysis, and speech recognition. TextBlob can carry out complex operations on text data, while NLTK provides simple access to millions of linguistic resources [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. TextBlob gives the Polarity and subjectivity of a given text. Polarity is between [-1 and 1], where \u0026minus;\u0026thinsp;1 shows the negative sentiment and 1 indicates the positive sentiment. When we apply TextBlob to all collected tweets, it classifies the positive and negative tweets. After classifying, we obtain the different ratios in positive and negative tweets. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (a) demonstrates all tweets, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (b) shows the positive and negative tweets, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (c) explains the ratio between positive and negative tweets. It is clear from Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (c) the positive tweets are 78%, and the negative tweets have only 22% that show users have positive reviews about quantum.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e6.2 VADER Analysis\u003c/h2\u003e \u003cp\u003eThis section discusses the findings of a sentiment analysis conducted on Twitter using VADER's technologies. According to the VADER sentiment Analyzer, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the sentiment score for each tweet as either positive or negative. After the limitations were applied, the categorization of the tweets as positive and negative is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (b). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (b) demonstrates how we might classify tweets directly as positive or negative using VADER if we selected the proper threshold value. However, curiously, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (c), 81% of the tweets conveyed favorable thoughts, while just 19% said the opposite. Comparing positive and negative tweets, the positive proportion is higher. Because the tiny number of tweets produced imbalanced data and the belief that the threshold value may provide a significant number of neutral viewpoints among all other classes. The results of this research point to the potential application of the VADER Sentiment Analysis to evaluate feelings expressed in tweets and categorize them appropriately, generating accurate results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Naive Bayes Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis research used three classifiers: Naive Bayes, Rocchio, and Perceptron, to assess and contrast their performance in predicting sentiment in Twitter comments related to quantum. A dataset of 12,538 tweets from diverse users was gathered, with each tweet labeled as positive or negative to ensure accuracy and reliability. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays status updates within each class, providing a sample of sentiments encountered during the study. The study aimed to reveal the comparative strengths and weaknesses of Naive Bayes, Rocchio, and Perceptron classifiers, allowing insights into their efficacy in sentiment analysis for quantum-related content on Twitter.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelect tweets from random users for labeling.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTweets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentiment Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantum! Impressive in different fields of software engineering! #Quantum \u0026#127744;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#Quantum computing's energy demands raise environmental worries, urging the need for sustainable solutions. #EnergyConsumption \u0026#128267;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe race for #Quantum advantage signals a promising future, where complex problems will find innovative solutions at lightning speed. ⚡\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#Quantum computing: the key to solving complex problems at an unimaginable speed. #GameChanger 🖥️\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantum computing's potential disruptions in data security raise concerns about privacy and encryption vulnerabilities. #SecurityRisk \u0026#128274;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrepare for a quantum-powered future that promises unrivaled technological advancements! #Quantum ⚡\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe era of #Quantum supremacy is upon us, promising unparalleled computational power and transformative technological advancements. \u0026#128161;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\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\u003eThe study divided the dataset into multiple partitions and used consistent training and testing sets across all classifiers. Performance evaluations were based on three metrics: precision, recall, and F-score. The capacity of a classifier to identify positive examples accurately is measured by precision. In contrast, the ability to identify all positive instances, including those that are predicted and missed, is calculated by a recall. When evaluating the classifier's performance, the F-score, a harmonic mean of precision and recall, considers both erroneous positives and false negatives. The comparative results of the classifiers are presented in tables, providing valuable insights into their performance in terms of precision, recall, and F-score. Analyzing these metrics can help conclude the effectiveness and robustness of the different classifiers used in the study.\u003c/p\u003e \u003cp\u003eThe calculations presented below have been utilized to compare the classifiers based on the precision, recall, and F-score performance measures. The Precision and Recall performance of Nave Bayes, Rocchio, and Perceptron are displayed in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e,\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, respectively.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{P}\\text{r}\\text{e}\\text{c}\\text{i}\\text{s}\\text{i}\\text{o}\\text{n}=\\frac{\\text{T}\\text{P}}{\\text{T}\\text{P}+\\text{F}\\text{P}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\text{R}\\text{e}\\text{c}\\text{a}\\text{l}\\text{l}=\\frac{\\text{T}\\text{P}}{\\text{T}\\text{P}+\\text{F}\\text{N}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\text{F}-\\text{s}\\text{c}\\text{o}\\text{r}\\text{e}=\\frac{2\\text{*}\\text{P}\\text{r}\\text{e}\\text{c}\\text{i}\\text{s}\\text{i}\\text{o}\\text{n}\\text{*}\\text{R}\\text{e}\\text{c}\\text{a}\\text{l}\\text{l}}{\\text{P}\\text{r}\\text{e}\\text{c}\\text{i}\\text{s}\\text{i}\\text{o}\\text{n}+\\text{R}\\text{e}\\text{c}\\text{a}\\text{l}\\text{l}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEqu. 1 and Equ.2 are used for finding the Precision and Recall value, while Equ.3 calculating the F-score.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNaive Bayes Precision and Recall Performance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaive Bayes Classifier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActual Positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActual Negative\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredicted Positive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredicted Negative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRocchio Precision and Recall Performance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRocchio Classifier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActual Positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActual Negative\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredicted Positive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredicted Negative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerceptron Precision and Recall Performance.\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\u003ePerceptron Classifier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eActual Positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActual Negative\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredicted Positive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredicted Negative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\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\u003eThe results of each classifier are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, which compares their levels of Precision, Recall, and F-score.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrecision, Recall, and F-score comparison of the three classifiers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNaive Bayes Classifier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRocchio Classifier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePerceptron Classifier\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrecision\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF-score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\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\u003eThe F-score reveals that the Naive Bayes classifier has the highest performance, whereas the perceptron classifier has the lowest. Although Rocchio's performance was almost comparable to that of Naive Bayes.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this paper, we have examined the use of quantum computing technology via sentiment analysis. Sentiment analysis has become increasingly important in understanding customer feedback and opinions in today\u0026rsquo;s digital world. Lexicon-based methods and supervised learning algorithms have been popularly used for this task. They require human-annotated text to train the classifier, while lexicon-based methods have recently been used for this task. This study evaluated sentiment analysis by labeling a distinct dataset, focusing on examining the positive impact of quantum innovation. It outperforms supervised learning classifiers and lexicon-based methods in a given dataset. Based on the findings, it is possible to draw the conclusion that Twitter users generally have favorable opinions towards the use of quantum computing technology. It would be beneficial to elaborate on how this increased speed may revolutionize various industries, enhance computing capabilities, or potentially introduce new applications for future implications. Moreover, discussing potential challenges, ethical considerations, or public concerns surrounding quantum technology would provide a more comprehensive response. In the future, we will use social media to interpret different types of innovative quantum-based technology to analyze present soft computing quantum software engineering technology impact.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Adel Assiri and Abdu Gumaei do methodology and formal analysis; Faisal Mehmood does data collection; and Sami Ullah does the final revision and English polishing. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare that they have no known competing \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e Not applicable as no human and or animal data is involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No Funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e Not Available.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePattanayak S, Pattanayak S (2021) Introduction to quantum computing, Quantum Machine Learning with Python: Using Cirq from Google Research and IBM Qiskit, pp. 1\u0026ndash;43\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNofer M, Bauer K, Hinz O, van der Aalst W, Weinhardt C (2023) Quantum Comput Bus Inform Syst Eng 65(4):361\u0026ndash;367\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNimbe P, Weyori BA, Adekoya AF (2021) Models in quantum computing: a systematic review. 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J Appl Data Sci 2(1):08\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErfina A, Nurul MR (2023) Implementation of Naive Bayes classification algorithm for Twitter user sentiment analysis on ChatGPT using Python programming language, Data \u0026amp; Metadata, vol. 2, pp. 45\u0026ndash;45\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonta V, Kumaresh N, Janardhan N (2019) A comprehensive study on lexicon based approaches for sentiment analysis. Asian J Comput Sci Technol 8(S2):1\u0026ndash;6\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Quantum Computing Technology, Sentiment Analysis, TextBlob, VADER, Supervised Learning","lastPublishedDoi":"10.21203/rs.3.rs-3999636/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3999636/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSentiment analysis is one of the most well-known applications of natural language processing (NLP) techniques used to determine a text's sentiment or emotional tone, such as a sentence, a paragraph, or an entire document. The goal of sentiment analysis is to identify and extract the underlying sentiment expressed by the author, whether positive or negative. Social media platforms like Twitter, Facebook, and Google\u0026thinsp;+\u0026thinsp;are quickly gaining popularity due to the ability for users to share and express their opinions on many subjects, engage in conversation with different organizations, and broadcast messages globally. Sentiment analysis has been extensively studied to track and understand developer comments and views. Quantum software engineering develops software for quantum computers, which use quantum computing to process data. It has gained significant prominence in the field of software technology. Quantum computing may tackle issues that classical computers cannot, advancing cryptography, optimization, and material science. This study aims to explore the social media user review for quantum computing technology innovation in the current era. For this purpose, sentiment analysis applies to social media user reviews for quantum computing technology use. The extracted data is scrubbed through preprocessing techniques. TextBlob, VADER, and supervised learning classification methods have analyzed the sentiments and topics extracted from social media. Results show that quantum users are satisfied with using this soft computing technology and find this experience a successful, positive review for innovative quantum computing technology.\u003c/p\u003e","manuscriptTitle":"Social Media User Evaluation for Quantum Computing Technology Via Sentiment Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 20:08:20","doi":"10.21203/rs.3.rs-3999636/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":"3320aea8-ac0f-4d95-affb-ffd500389605","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-06T14:16:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-05 20:08:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3999636","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3999636","identity":"rs-3999636","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}