Evaluation of Maternal Patient Experience Through Natural Language Processing Techniques: The Case of Twitter Data in The United States During COVID-19

Evaluation of Maternal Patient Experience Through Natural Language Processing Techniques: The Case of Twitter Data in The United States During COVID-19 | 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 Evaluation of Maternal Patient Experience Through Natural Language Processing Techniques: The Case of Twitter Data in The United States During COVID-19 Debapriya Banik, Sreenath Chalil Madathil, Amit Joe Lopes, Sergio A. Luna Fong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3881957/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 Purpose The healthcare sector constantly investigates ways to improve patient outcomes and provide more patient-centered care. Delivering quality medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback, such as HCAHPS, to measure patients' experiences. The power of social media can be harnessed using artificial intelligence and machine learning techniques to provide researchers with valuable insights into understanding patient experience and care. Our primary research objective is to develop a social media analytics model to evaluate the maternal patient experience during the COVID-19 pandemic. Method We used the "COVID-19 Tweets" Dataset, which has over 28 million tweets, to evaluate patient experience using Natural Language Processing (NLP) and extract tweets from the US with words relevant to maternal patients. The maternal patient cohort was selected because the United States has the highest percentage of maternal mortality and morbidity rate among the developed countries in the world. Results We created word clouds, word clustering, frequency analysis, and network analysis of words that relate to “pains” and “gains” regarding the maternal patient experience, which are expressed through social media. Conclusion This model will help process improvement experts without domain expertise understand various domain challenges efficiently. Such insights can help decision-makers improve the patient care system. We also conducted a preliminary study to discover if a particular group faces racial health inequity. Maternal health patient experience natural language processing sentiment analysis healthcare systems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction As healthcare organizations focus on improving patient-centered care, patients play a more active role in decision-making that prioritizes the patient’s needs, values, and preferences. Patient-centered care is defined by the Institute of Medicine (IOM) as offering treatment that considers and responds to each patient's unique preferences, requirements, and values and ensures that the patient's values direct all clinical judgments [ 1 ]. The organization suggests a total of six goals, which are safe, effective, patient-centered, timely, efficient, and equitable [ 1 ]. Research has shown that patient-centered care improves patient satisfaction, outcomes, communication, and collaboration between patients and healthcare providers [ 2 ]. This approach recognizes that patients are the key decision-makers in their care and aims to involve them as much as possible. As a result, the trend towards patient-centered care has been emphasized, as patients seek a more engaged experience with the use of health monitoring devices and a trusted relationship with their healthcare provider [ 3 ], [ 4 ], [ 5 ]. Patients’ experience is another significant quality index. There is a strong positive correlation between the outcome of a patient and the experience [ 6 ]. A patient experience relies on what happened to the patient and how the patient perceived that experience. This also highlights the subtle difference between 'patient experience' and 'patient satisfaction,' which are often used interchangeably. Moreover, the patient experience is related to their perception of care, and satisfaction accounts for their expectations of care. A vast amount of unstructured data regarding patients' healthcare experiences is present on social media, which is usually posted by patients or their family members [ 7 ], [ 8 ]. However, due to the ingrained complexity of processing and analyzing such data, this information is not systematically assessed and utilized to improve the healthcare system. Text analytics can play a more significant role in harnessing meaningful insights from social media; policymakers can get directions on improving and implementing better patient-centric care. This study incorporates Natural Language Processing (NLP) to efficiently capture maternal patient experience from a large-scale social media dataset containing Twitter tweets. We selected maternal patients as one of the cohorts to analyze the use of NLP to measure patient experience. Moreover, maternal health is critical to women's health and well-being, particularly in the United States (US), where maternal mortality rates are high. According to the Centers for Disease Control and Prevention (CDC), approximately 700 women die each year in the US due to pregnancy-related causes, and 60% of these deaths are preventable [ 9 ]. We analyze tweets to discover topics related to maternal health and their sentiments to understand the pains and gains expressed by patients, relatives, or friends. We used the IEEE coronavirus COVID-19 tweets dataset for our analysis [ 10 ]. We also examined the data within the healthcare disparity lens to find topics related to pregnancy, maternal care, and COVID-19. In summary, the main research objectives of this article are as follows: Utilize the NLP algorithm to evaluate patient experience related to maternal health using social media data Classify text data to various topics relevant to maternal health and conduct sentimental analysis Literature Review We conducted a literature review to understand how patients’ experience was captured and what tools and techniques are used to garner the patients’ experience. We also discuss studies focusing on NLP techniques, specifically topic modeling and sentiment analysis. 2.1 Research in Patient-centered Care and Patient Experience Improvement Numerous healthcare organizations have undertaken patient-centered care as part of their mission and strategy when IOM announced patient-centered care as one of its six objectives for improving healthcare [ 11 ]. Over the past decade, there has been a significant advancement in evaluating patients' experiences, demonstrating the value of incorporating the patients' insights and demands into the healthcare system [ 12 ]. Patient-centered care includes showing respect toward patients’ values and choices [ 13 ], [ 14 ], integrating care coordination and improving access to care [ 15 ], [ 16 ], [ 17 ], educating patients on their clinical status, prognosis, and progress throughout their journey [ 17 ], [ 18 ], providing physical comfort and emotional support [ 19 ], [ 20 ], [ 21 ], and ensuring patients care continuation and well-being during and after discharge [ 16 ], [ 22 ]. A lack of patient-centered care will result in unmet patient needs, waste of resources, and ineffective care [ 23 ]. Several researchers found that patient-centered care was associated with improved patient outcomes, which include improved quality of life [ 24 ], [ 25 ]. Since the healthcare industry is becoming increasingly patient-centric, a need exists to quantify, record, and improve patients' experience under their care [ 26 ], [ 27 ]. Patients' experiences of the care and the feedback extracted from patients about those experiences are integrated to conceptualize patient experience and satisfaction, which are crucial for improving healthcare systems. According to[ 28 ] Press (2014), incorporating patients' experiences and evaluating the patients' nonclinical needs can improve healthcare systems and reduce malpractice claims. In the US, the federal government entities, the Centers for Medicare and Medicaid Services (CMS) and the Agency for Healthcare and Research Quality (AHRQ), developed a survey named Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) [ 29 ], [ 30 ] to assess the patient satisfaction. The patient satisfaction metrics should consider evaluating the cognitive and emotional aspects of patients' satisfaction that impact the clinical outcomes [ 31 ], [ 32 ]. Patient experience can be measured using journey mapping, qualitative and quantitative surveys [ 33 ], [ 34 ], artificial intelligence techniques, technology, and digital tools [ 35 ], [ 36 ], [ 37 ]. The evaluation of patient experience helps healthcare organizations extract actionable insights to improve healthcare quality [ 38 ], [ 39 ], [ 40 ], increase engagement with the patient and their healthcare provider [ 41 ], [ 42 ], improve care effectiveness, lower employee turnover, and enhance employee satisfaction [ 43 ], [ 44 ], [ 45 ], [ 46 ]. Studies have also shown that patient experience can impact critical financial levels dependent on patient retention and medical malpractice claims [ 46 ], [ 47 ], [ 48 ]. 2.2 Research in Text Analytics Text analytics collect trends, insights, sentiments, and topics of interest using an automated process of drawing information from unstructured data and take advantage of tools, methods, and mathematical algorithms to analyze and make computers understand text and speech [ 49 ]. It can differentiate between positive and negative emotions from the text [ 50 ], discussed topics [ 51 ], and the association between the keywords. Text analytics contain various applications, such as descriptive, prescriptive, or predictive analytics [ 52 ], [ 53 ]. One of the most significant methodologies of text analytics is Natural Language Processing (NLP). NLP, a branch of Artificial Intelligence (AI), essentially gives computers the power to understand human language from written text and spoken words, similar to how an ordinary person would understand [ 49 ], [ 54 ]. It allows computers to perform a series of processes to disintegrate the human text and comprehend human language by consolidating computational linguistics and machine learning tools [ 55 ], [ 56 ]. For instance, researchers have used NLP in various healthcare applications, including a large-scale analysis of the counseling conversation to provide effective counseling to patients [ 57 ], evaluation of health insurance claims to find fraud or abuse [ 58 ], assessment and rehabilitation of patients during the COVID-19 pandemic, digitization and classification of prescriptions [ 59 ], and extraction of patient information from electronic healthcare records [ 60 ], and patients’ sentiments from healthcare surveys [ 38 ], [ 61 ]. This review discusses topic modeling, sentimental analysis, and n-gram analysis. 2.2.1 Topic Modeling Topic modeling is an effective and practical tool in NLP for analyzing large text documents [ 62 ]. Topic modeling automatically groups words into topics and identifies relationships between documents within a dataset. In other words, it aims to characterize a text (such as articles, social media postings, survey results, and interview responses) as a distribution over topics and the topics as a distribution over words. The number of topics and the words within each topic are measured using a coherence score, and the similarity of these words to each other is measured [ 63 ]. Latent Dirichlet Allocation (LDA) is one of the most widely used topic modeling algorithms [ 64 ], [ 65 ], [ 66 ], [ 67 ], [ 68 ] to represent a group of documents based on their underlying themes. It is an unsupervised learning method; i.e., the topics are identified without prior knowledge of their content [ 69 ]. LDA utilizes the coherence score to measure the optimal number of topics that provide the maximum coherence value [ 70 ]. LDA aims to determine the range of issues in a specific document. LDA has been applied to a wide range of NLP tasks, which include topic classification from journals and newsgroups [ 67 ], [ 71 ]. LDA has also been widely used in the medical and healthcare sectors to analyze and classify various types of healthcare data. Applications of LDA in healthcare include analyzing electronic health records (EHRs) and patient feedback responses to prioritize patient experience improvement initiatives. LDA can analyze EHRs that contain a wealth of information, including clinical notes, diagnosis codes, and laboratory results, to identify patterns in patient data, such as comorbidities, medication use, side effects, disease progression, and treatment outcomes [ 72 ], [ 73 ], [ 74 ], [ 75 ]. Several researchers used LDA to analyze patient feedback from hospital surveys and online reviews to identify factors influencing patient satisfaction. They found that topics such as communication, nursing care, staff attitude, care quality, waiting time, facility quality, and overall experience impact patient reviews of the hospital [ 76 ], [ 77 ], [ 78 ]. Improvements to various systems, such as appointment scheduling and billing, are crucial for improving the patients' experience. The use of social media analysis in evaluating patient experience utilizes LDA techniques. [ 69 ] Okon et al. (2020) used LDA to analyze over 176,000 Reddit threads that provided feedback about dermatology patients' experiences. Ortega (2021) [ 22 ] utilized patient feedback from social media (Twitter and Reddit) and applied LDA to evaluate breast cancer patients’ experiences, find the latent topics shared by the patients, and evaluate the sentiments behind those topics. In addition to identifying topics related to patient experience, LDA can also be used to analyze changes in patient experience over time. Ao et al. (2020) [ 79 ]used LDA to analyze patient feedback over three years to identify changes in patient experience. They found that while topics related to communication and staff attitude remained consistent, topics related to waiting time and access to care became more prominent in later years. 2.2.2 Sentiment Analysis One of the most widely used tools to garner sentiment from text or voice messages is Sentiment Analysis, which classifies the underlying emotion as positive, negative, and neutral [ 80 ], [ 81 ], [ 82 ], [ 83 ]. We discuss three sentimental analysis methods: machine learning algorithms, rule-based systems, and lexicon-based approaches. A Lexicon-based approach uses dictionaries of sentiment-laden words to classify text based on the presence or absence of particular words or phrases and utilize the term phrases, sentimental idioms, and expressions. On the other hand, a machine learning-based approach uses a computer model to train an existing dataset with defined emotions and use those trained models to predict the sentiment of new text data [ 84 ]. The rule-based systems use a set of predefined rules to classify text based on specific patterns. The Valence Aware Dictionary and sEntiment Reasoner (VADER) is a sentiment analysis tool based on the rule-based system specifically designed to handle social media data, which often contains informal language (often used in social media texts), slang, and abbreviated words. It has gained significant attention recently due to its high accuracy and efficiency in analyzing sentiment in social media texts [ 85 ]. They showed that VADER’s accuracy in identifying neutral sentiments outperformed other sentiment analysis tools, such as TextBlob and the Stanford CoreNLP. Elbagir and Yang (2019) [ 86 ]used VADER and NLTK sentiment analysis tools to interpret sentiments in Twitter data. Here also, VADER achieved higher accuracy and was more capable of handling contextual information than NLTK. A. Kumar et al. (2020) [ 87 ] showed that VADER performed better than SentiStrength and AFINN in analyzing sentiments, irony, and sarcasm in online product reviews. Our study used the VADER sentiment analysis to analyze maternal patient experience from Twitter. Different industries, such as tourism, politics, and marketing, extensively utilize the power of sentiment analysis [ 88 ] to discern and extract subjective insights from customer reviews and utilize those insights to improve their service [ 89 ]. Sentiment analysis is widely used in clinical research and health informatics to analyze patients’ sentiments regarding their care and identify areas to improve healthcare quality. Asghar et al. (2016) [ 90 ]have reviewed several use cases for sentimental analysis in healthcare. Sentiment analysis gives decision-makers insights into how patients feel toward caregivers and treatment systems [ 91 ]. Greaves et al. (2014) [ 92 ] presented a mixed-method study to evaluate the patient experience and hospital quality from a small number of tweets. Hawkins et al. (2016) [ 93 ] utilized a machine learning approach to analyze data from 2,349 US hospitals over one year to determine the patient's experience, including their care, experience from hospital administration, and interaction with healthcare professionals. Crannell et al. (2016)[ 94 ] presented a study that analyzed emotions from tweets of various cancer patients for unique cancer diagnostics. Similarly, Rodrigues et al. (2016) [ 95 ]have introduced a tool called SentiHealth-Cancer (SHC-pt) to identify the mental condition of cancer patients from social media. While surveys and patient feedback are commonly used to measure patient experience, patient journey mapping can capture it more comprehensively. Ortega (2021) [ 22 ] utilized sentimental analysis to provide valuable insights for improving empathetic and respectful care in clinical systems and enhancing patient-centered care. Polarity and subjectivity are the two most popular measures of sentiment analysis [ 96 ], [ 97 ], [ 98 ]. Subjective texts often have more complex and nuanced meanings than objective ones [ 99 ], [ 100 ]. For instance, a positive review of a restaurant may contain various subjective expressions such as "the food was amazing," "the ambiance was fantastic," or "the service was outstanding." Subjectivity in sentiment analysis refers to the extent to which a text expresses personal opinions, feelings, or attitudes [ 99 ]. Polarity scores help to identify the text's mood (positive, neutral, or negative sentiments). There are different approaches to detecting subjectivity and polarity in sentiment analysis. One common practice is to use lexicons or dictionaries that contain words with positive or negative connotations. This method involves assigning a sentiment score to each word in the text based on its polarity and then combining these scores to obtain an overall sentiment score. Another approach is to use machine learning algorithms, such as support vector machines or neural networks, that are trained on a dataset of annotated texts to predict the sentiments of new texts. After the sentiment analysis, the N-gram analysis was conducted for every topic depending on the polarity. N-gram analysis is a text mining technique used to analyze the structure and content of written language [ 101 ], [ 102 ], [ 103 ]. An N-gram is a contiguous sequence of n items from a given text sample, where n is an integer representing the number of items in the sequence [ 103 ]. Table 1 summarizes the literature review based on the tools, techniques, and applications for sentimental analysis and LDA. From the discussion of the relevant literature, we find that there needs to be a research gap in evaluating efficient patient experience using social media analytics. Although several studies focused on data collected from social media, they always needed a more efficient and effective way of sentiment analysis. Table 1 Summary of Literature Review Publications Sentiment Analysis Topic modeling using Latent Dirichlet Allocation (LDA) ML approach Lexicon based approach Online reviews or social media Electronic health record Patra et al. (2021) × Li et al. (2022) × Fairie et al. (2021) × Hao et al. (2017) × Ji et al. (2019) × Ortega, (2021) × × Okon et al. (2020) × Chintalapudi et al., 2021 × × Greaves et al. (2014) × × Crannell et al., 2016 × × Rodrigues et al. (2016) × × Mouthami et al., 2013 × × Elbagir & Yang, (2019) × × Asghar et al. (2016) × × Kumar et al. (2020) × × This study aims to evaluate the maternal patient experience during the COVID-19 pandemic from social media. To achieve this objective, we formulated an NLP algorithm to discover the dominant topics patients express on Twitter and the sentiment behind them and measure patient experience. Methodology To evaluate the patient's experience from Twitter, the study follows a properly defined series of steps to extract the common topics maternal patients talk about and then assess the sentiment behind them. We used Python 3.5 in Jupyter Notebook and relevant packages for our analysis. At first, data preprocessing was done to make the data ready for Natural Language Processing algorithms. In this study, we utilized the Tweets published in an IEEE source [ 10 ] that were collected using several COVID-19-related keywords. A subset of the dataset of 28,087,954 tweets was used in this study. These tweets are specifically from the users who posted regarding the COVID-19 pandemic. Figure 2 shows a high-level overview of the study. 3.1 Data Preprocessing First, the tweets are preprocessed using R programming, which filters out the tweets posted only from the USA. Several R packages, such as lubridate, dplyr, plyr, and tidyr, were used. Then we used several relevant keywords identified for maternal patients [ 22 ], [ 104 ], [ 105 ], [ 106 ] such as 'maternal', 'nursing', 'maternal_health', 'pregnancy', 'preeclampsia','pre-eclampsia', 'infant', 'motherhood', 'gynecology', 'postpartum', 'maternalmentalhealth', 'maternal_mortality', 'obstetrical', 'womenshealth', 'doula','obstet', 'pregnancyrelated', 'gynecology', 'cesarean', 'preterm', 'pregnancyrelated', 'gynecol', 'perinatal', 'blackmaternalhealth', 'childbirth', 'pregnant', 'mentalhealth', 'breastfeeding', 'momlife', 'birth', 'baby', 'blackmamasmatter', 'healthcare', 'newmom', 'newborn', 'fourthtrimester', 'maternalhealthmatters', 'birthworker', 'postnatal', 'postpartumjourney', 'midwife', 'maternitycare', 'midwives', 'mother', 'holisticpregnancy', 'maternal', 'breastfeedingmom', 'reclaimlabor', 'charlestonsc', 'healthypregnancy', 'educateyourself', 'reclaimbirth', 'postpartumsupport', 'informeddecisions', 'intentionalbirth', 'reclaimourbodies', 'perinatalmentalhealth', 'birthinpower', 'birthsupport', 'selfcare', 'antenatal', 'antenatal care' were used to further filter out the tweets. 3.2 NLP Pipeline After preprocessing the dataset, selected tweets were isolated and fed into the NLP pipeline. There are several steps for the NLP, which are described in a flow chart in Fig. 3 . The further processing uses the NLTK package to remove unnecessary stop words, emoji, and punctuations from the raw tweets that do not convey any meaning for the topic modeling. Then, the words are tokenized, and then lemmatization is done using Python's NLTK library stemming. In lemmatization, words are converted into the base form for efficient analysis. This is useful for normalizing words and reducing inflected forms to a common base, thus aiding in text analysis and comparison. For instance, if the original word is “running” after lemmatization, it would be “run.” Each word was tagged with its respective part of speech. Parts of speech tagging are fundamental tasks in natural language processing (NLP). It involves assigning each word in a sentence with its corresponding grammatical category or part of speech (e.g., noun, verb, adjective, etc.). This process helps to extract meaningful information and understand the syntactic structure of a sentence. After this step, the dataset is ready for the Topic Modeling algorithm. 3.2.1 Topic modeling Since the inputs to the topic modeling are tweets, the model characterizes tweets as a distribution over topics and topics as a distribution over words. Essentially, this means that a tweet is assigned a probability for each topic, and each topic is assigned a probability for each word. We used LDA to determine the likelihood of a given string, whether a sentence or a document, using the likelihood of the string within the domain. Blei et al. (2003) [ 67 ]explained the following generative process for LDA: Randomly select a distribution over topics for every tweet For every word in the tweet: Randomly select a topic from a distribution over topics in step 1 From the corresponding distribution over the vocabulary, randomly select a word In this study, LDA was implemented using Python’s genism package. Python's NLTK and genism packages used processed tweets to identify the specified topics. We select the optimal number of topics based on the coherence score for each topic that provides the maximum coherence value. An inter-topic distribution mapping is used to visualize the topics. 3.2.2 Sentiment Analysis We reiterate that the primary objective of sentiment analysis is to categorize a specific sentence or block of text as positive, negative, or neutral. We used TextBlob and VaderSentiment packages of Python to perform sentimental analysis on the Twitter data after we classified them into specific dominant topics. We used polarity and subjectivity to quantify sentiment analysis [ 96 ], [ 97 ], [ 98 ]. We used the VADER tool for analyzing sentiments that follow a set of rules. The analysis classifies the text's tone as positive, negative, or neutral. The overall sentiment score produced by VADER is a continuous value that ranges from − 1 to 1, where − 1 indicates highly negative sentiment, 0 indicates neutral sentiment, and 1 shows extremely positive sentiment. VADER also generates scores for the three sentiment categories (positive, negative, and neutral) and a compound score. This normalized weighted composite score represents the overall sentiment of the text on a scale from − 1 to 1. In this study, we utilized the lexicon-based approach to quantify the subjectivity of every tweet using Python’s TextBlob package. After the sentiment analysis, the final output contains every tweet classified into a topic along with the sentiment behind that topic. 3.2.3 N-gram Analysis The sample text is divided into N-grams in N-gram analysis, which are then counted and analyzed to determine their frequency and distribution. The most common form of N-gram analysis is the bigram (n = 2), which considers pairs of adjacent words in the text. This type of analysis helps identify patterns and relationships between words in the text, such as common collocations or idiomatic expressions. Trigram (n = 3) analysis considers three adjacent words, and higher-order N-grams consider even more words. N-gram analysis is widely used in various applications, such as natural language processing, information retrieval, and machine learning. For instance, it can be used to identify the most frequently occurring words in a given text, to identify patterns in the usage of certain words or phrases, or to develop language models that can predict the next word in a sentence based on the preceding N-grams. Results At the first stage of data preprocessing from 28 million tweets, around 31,438 tweets were extracted about maternal healthcare and from the USA. After the stop word removal and lemmatization, the processed tweets were transferred to the next step, topic modeling. From the LDA topic evaluation that uses different values of ‘number of topics,’ we find the optimal number of topics to be three. The LDA model coherence values are shown below in Fig. 4 . In this figure, we can see that for the number of topics 3, the model generated the maximum coherence score of 0.3466. After that, the coherence score decreases as the number of topics increases. Hence, the number of topics for the LDA model was set to three, which are visualized in different ways. At first, a word cloud was drawn for every topic depicted in Fig. 5 . The keywords inside these word clouds represent each topic. Therefore, the frequency of the most represented keywords in each topic is depicted in the following bar charts in Fig. 6 . It is evident that in Topic 1, “fever, baby, mother, birth” are most frequent. In contrast, Topic 2 has “healthcare, high, help, worker, public, health medical” most frequently. In Topic 3, “coronavirus, positive, flu, birth, pregnant, test” are most frequent. From the above analysis, labels can be assigned to every topic. Depending on the keyword frequency in Topic 1, users talking about pregnant mothers and newborn baby’s mothers are worried about fever; in Topic 2, impact on healthcare facilities and workers and consequences on expectant mothers; and in Topic 3, tweets are concerned about the rising flu, which could be coronavirus-positive cases, COVID-19 testing, the rapidly spreading virus, and its effect on pregnant mothers. Depending on these keyword frequencies, every tweet is classified into different topics, shown in three colors in Fig. 7 . Here, orange, green, and blue tweets represent three topics based on their keywords. Blue represents topic 1, orange corresponds to topic 2, and green represents topic 3. Next, the distribution of each topic in the dataset is shown in Fig. 8 . Here, it is evident that most of the tweets fall under Topic 2 and Topic 1. Furthermore, all the dominant topics were shown in an inter-topic distance map to visualize better how topics are classified. This is illustrated in Fig. 9 . Furthermore, the three topics were again visualized using t-SNE, shown in Fig. 10 . Each dot represents a tweet, and the three colors indicate different topics. In the t-sne graph, the n-dimensional data is described in two-dimensional space. It is clear from the figure that there is little overlapping between topics. After classifying the tweets, the sentiment behind each tweet was measured concerning polarity and subjectivity, as in Table 2 . The value of polarity ranges from − 1 to 1. The value of -1 represents highly negative experience, 1 represents good experience, and 0 represents neutral experience. For subjectivity, the value ranges from 0 to 1. If a tweet contains a more significant amount of personal opinion, the subjectivity value would be close to 1. The value would be close to 0 if it contains factual information. Table 2 Sentiment Analysis Dominant Topic Topic_Perc_Contrib Actual tweet Polarity Vader subjectivity 2 0.7592 @BMaienschein Thank you for seeing San Diego's ME/CFS patients and our many community volunteers and supporters. At this point, we have donated 1620 masks, caps, and other PPE mostly to San Diego healthcare workers and organizations. https://t.co/CwaTq2TqwW #MECFSSD #aCure4ME https://t.co/6k54mUBPwr 0.6597 0 2 0.5576 East Texas doctors discuss possible pandemic baby boom: https://t.co/00LA8Ny7Tu https://t.co/sTDYNLqwWJ 0 1 3 0.5631 @realDonaldTrump @CDCgov Covid kills! This is not fake news people are dying. My brother lost his wife to Covid, mother of 7. Really Trump... -0.5481 0 1 0.879 Can’t wait for covid to be over. My baby wanna go to NYC so bad I can’t wait to get her there -0.6696 0.6666 In Table 3 below, the mean polarity and subjectivity values are calculated. The mean polarity of Topics 1 and 3 conveys negative emotion, and the other topic has fairly positive emotion. However, as the negative and positive polarity tweets negated each other, each topic must be studied separately according to sentiments. Table 3 Average polarity and subjectivity depending on topics Dominant topic Average polarity Average subjectivity 1 -0.003856237 0.3548346 2 0.065616866 0.3669842 3 -0.199848470 0.3639693 First, sorting the dataset for only Topic 1 with negative polarity, which essentially means negative sentiment, and running an N-gram analysis presents the following, as shown in Table 3 . The N-gram analysis in Table 4 shows that mothers worry about their babies catching a fever and how pregnant mothers are severely affected by COVID-19. On the other hand, tweets on the same topic that express positive sentiment mostly talk about how pregnant mothers are surviving the coronavirus. Even during the COVID pandemic, they are optimistic about safe childbirth and express hope for healthy newborns. The N-gram analysis of that portion is shown in Table 5. In the case of Topic 2, tweets that convey negative emotion talk mainly about how healthcare providers are having a tough time due to the risks and a considerable number of patients, as shown in Table 6. However, the tweets with positive sentiment talk about helping healthcare agencies tackle the spread of the virus, as shown in Table 7. If focused on the N-gram analysis of the negative emotions’ tweets of Topic 3, we found that the mothers are worried about their babies testing positive for coronavirus, as shown in Table 8. On the contrary, the positive tweets of Topic 3 usually talk about how mothers are getting care from healthcare workers, and the prevention strategy of widespread testing taken by the healthcare facilities is depicted in Table 9. 4.2 Preliminary Analysis of Disparity We also utilized this study to conduct a preliminary analysis of the use of social media analysis to capture healthcare disparities. Upon careful examination of the tweets with a specified list of keywords regarding healthcare inequality, we found that most tweets focused mainly on racial disparities in healthcare. A word cloud is depicted in Fig. 11 to show the racial disparity-related tweets. Topics such as minority, race, and distrust are frequent in these tweets. An N-gram analysis of the following tweets found that African-American mothers are worried about not getting proper healthcare services. The N-gram analysis is shown in Table 10 Some of the actual tweets posted by the users are depicted in Fig. 12 . These representative tweets say a great deal about the racial injustice in healthcare in the USA, which was especially aggravated during the COVID-19 pandemic. 4.3 Limitations of This Study However, there are a few limitations associated with analyzing Twitter data. For instance, users often use non-standard text, incorrect English, multilingual content, and emojis to express their opinions, which makes it incredibly challenging for NLP to classify some of the tweets and calculate the sentiment accurately. Also, users use sarcasm to express their views, which is very complex for the program to understand. There are also spelling mistakes or incomplete sentences, which makes it difficult for the model to understand the underlying sentiment. It is essential to preprocess the data to ensure that these algorithms provide valuable information. However, computers may not be able to distinguish between similar words such as "doctor" and "physician" or identify that "meal" and "meals" convey the same information. Additionally, spelling errors may create further complications because a minor modification in a word can make the computer perceive it as entirely different. These constraints are solved by using stemming or lemmatizing to reduce words to their base form, utilizing NLP libraries to correct spelling errors, manually analyzing comments to identify context-specific words, and replacing them with common words. Preprocessing patient-derived data may be more complex because respondents may need more healthcare literacy, make spelling mistakes, or use texting language outside the NLP library used for processing. Besides, sometimes tweets contain phrases that need to convey the words' literal meaning, making it difficult for the program to understand. For instance, users who use the phrase ‘baby fever’ usually express the longing that some people experience that relates to their desire to have a child. Additionally, isolating unique tweets from millions of tweets is challenging as there are several occasions of retweeting or quoting another person's tweet. However, the computer program interprets the literal meaning of the phrase, so the program fails to extract the proper sentiment from tweets. Nevertheless, despite all these limitations, the model produces an initial insight that gives policymakers a way to capture patient experience to improve healthcare systems and understand system-level concerns for the under-represented communities. Conclusions In this study, we have analyzed the tweets to extract maternal patient experience and classified the different topics patients are talking about and the sentiment behind each of them. As we can see, topics have distinct levels of sentiment associated with them, which shows the diversity of patient experience. Another aspect to note is that on social media, patients and their relatives post their experiences, giving various perspectives on medical care. The observed result from the LDA model classifies tweets into different topics, which helps to narrow down the patient's experience. The sentiment analysis also provides meaningful information regarding the maternal patient experience. Moreover, through the N-gram analysis, we delivered what is discussed in each topic and critical insights regarding racial inequity in healthcare systems. The analysis of this study would work as a guideline for policymakers inside a healthcare system. As among the developed countries in the world, the US has the highest rate of maternal mortality or morbidity; analysis of patient experience can provide valuable insights regarding patients' expectations of care and the original quality of care that they received. Utilizing the topics identified using topic modeling, policymakers, and community organizations can understand the patients' negative and positive experiences and develop policies accordingly. Moreover, social media analytics provides an initial insight into the healthcare disparities from the system-level point of view that should be captured in patient experience surveys or other tools. One recommendation for future work is to include other social media data and data from different languages. Because there are many multilingual users in social media, the model would be able to capture more information. Another important addition can be using Process mining techniques to explore the patient journey map and take necessary action to improve the quality of care. Suppose policymakers have access to the surveys of the patients’ experience collected through interviews. In that case, this data can be incorporated into the model to obtain much more insights that improve healthcare quality. Typical conditions such as postpartum blues and physician burnout may have caused the negative patient experience; COVID might have added to the negativity of experiences during these times. Declarations Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding declaration This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. 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Luna Fong","email":"","orcid":"","institution":"The University of Texas at El Paso","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"A. Luna","lastName":"Fong","suffix":""},{"id":270665025,"identity":"e504bcb0-1810-4702-be68-30957142c1bf","order_by":4,"name":"Santosh K. Mukka","email":"","orcid":"","institution":"Lourdes Pediatrics","correspondingAuthor":false,"prefix":"","firstName":"Santosh","middleName":"K.","lastName":"Mukka","suffix":""}],"badges":[],"createdAt":"2024-01-20 15:59:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3881957/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3881957/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50746333,"identity":"cca8df33-7feb-4e84-9bbd-920f8e7feeed","added_by":"auto","created_at":"2024-02-06 17:06:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170553,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2: High-Level Overview of the Framework\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/1c0074d500da08648a470f90.png"},{"id":50746336,"identity":"64b99b8c-5fed-4c33-9f2d-406a1736a9d5","added_by":"auto","created_at":"2024-02-06 17:06:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160231,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3: Flowchart of NLP pipeline\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/6ca41475269febaa7f9dead7.png"},{"id":50746334,"identity":"d913e982-41fc-4cb8-aeb3-f14d4402cc56","added_by":"auto","created_at":"2024-02-06 17:06:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17908,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4: Coherence score of LDA model for different number of topics\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/201aad0826227e455e2f97df.png"},{"id":50748336,"identity":"757c10a6-d50e-4fde-8879-4903e9cde9cd","added_by":"auto","created_at":"2024-02-06 17:14:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":344740,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5: Word cloud of three topics\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/c46b3aa940f23a9d00455f2c.png"},{"id":50749161,"identity":"362f9897-511d-494f-ad5b-14c1ff1b8f13","added_by":"auto","created_at":"2024-02-06 17:22:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":226348,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 6: Word count and importance of topic keywords for each topic\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/5c884c56dd306953220ea569.png"},{"id":50748339,"identity":"a5774808-486c-4d8f-a134-a8c814a0300d","added_by":"auto","created_at":"2024-02-06 17:14:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":84301,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7: Tweets classified into three topics\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/bc82302c10f2e2fda943ff4b.png"},{"id":50749160,"identity":"fa0adb7f-981d-49a6-b0aa-1ad15ebfa979","added_by":"auto","created_at":"2024-02-06 17:22:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":33779,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 8: Distribution of three topics\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/e2f68dd916a7c1106f48bc92.png"},{"id":50746332,"identity":"cf6751e1-bf68-488d-a8d5-3afec24f3ad1","added_by":"auto","created_at":"2024-02-06 17:06:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":58500,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 9: Inter-topic distance map\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/4b72954ecfb6cc15b765c230.png"},{"id":50746340,"identity":"ac190cfd-707c-45ef-8e98-753f5cd1d466","added_by":"auto","created_at":"2024-02-06 17:06:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":168116,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 10: Clustering of tweets\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/0df1fdce0f53cdfdd02d9353.png"},{"id":50746343,"identity":"00a9608c-bf51-4c7e-9f6b-be0575f41e3d","added_by":"auto","created_at":"2024-02-06 17:06:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":228739,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 11: Word cloud of the tweets about racial inequality in healthcare\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/cf9e4529cbab034560521b62.png"},{"id":50748337,"identity":"38c3c9b4-d398-4b38-a455-d8e3432c0a61","added_by":"auto","created_at":"2024-02-06 17:14:51","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":74455,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 12: Tweets regarding maternal healthcare inequality\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/e1096b9385fdcb7eb41774a6.png"},{"id":50751551,"identity":"6bf2dade-b155-4697-b6ff-4ff0ab41e84b","added_by":"auto","created_at":"2024-02-06 17:38:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1779990,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/0f09c5bd-d370-42e6-9a00-5adbca3af34a.pdf"},{"id":50748340,"identity":"f1f88059-491a-41cd-9c41-a6c464636850","added_by":"auto","created_at":"2024-02-06 17:14:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":215716,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3881957/v1/ee9c81ecfc4a809325ec138e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Maternal Patient Experience Through Natural Language Processing Techniques: The Case of Twitter Data in The United States During COVID-19","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs healthcare organizations focus on improving patient-centered care, patients play a more active role in decision-making that prioritizes the patient\u0026rsquo;s needs, values, and preferences. Patient-centered care is defined by the Institute of Medicine (IOM) as offering treatment that considers and responds to each patient's unique preferences, requirements, and values and ensures that the patient's values direct all clinical judgments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The organization suggests a total of six goals, which are safe, effective, patient-centered, timely, efficient, and equitable [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Research has shown that patient-centered care improves patient satisfaction, outcomes, communication, and collaboration between patients and healthcare providers [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This approach recognizes that patients are the key decision-makers in their care and aims to involve them as much as possible. As a result, the trend towards patient-centered care has been emphasized, as patients seek a more engaged experience with the use of health monitoring devices and a trusted relationship with their healthcare provider [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients\u0026rsquo; experience is another significant quality index. There is a strong positive correlation between the outcome of a patient and the experience [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A patient experience relies on what happened to the patient and how the patient perceived that experience. This also highlights the subtle difference between 'patient experience' and 'patient satisfaction,' which are often used interchangeably. Moreover, the patient experience is related to their perception of care, and satisfaction accounts for their expectations of care. A vast amount of unstructured data regarding patients' healthcare experiences is present on social media, which is usually posted by patients or their family members [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, due to the ingrained complexity of processing and analyzing such data, this information is not systematically assessed and utilized to improve the healthcare system. Text analytics can play a more significant role in harnessing meaningful insights from social media; policymakers can get directions on improving and implementing better patient-centric care.\u003c/p\u003e \u003cp\u003eThis study incorporates Natural Language Processing (NLP) to efficiently capture maternal patient experience from a large-scale social media dataset containing Twitter tweets. We selected maternal patients as one of the cohorts to analyze the use of NLP to measure patient experience. Moreover, maternal health is critical to women's health and well-being, particularly in the United States (US), where maternal mortality rates are high. According to the Centers for Disease Control and Prevention (CDC), approximately 700 women die each year in the US due to pregnancy-related causes, and 60% of these deaths are preventable [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. We analyze tweets to discover topics related to maternal health and their sentiments to understand the pains and gains expressed by patients, relatives, or friends. We used the IEEE coronavirus COVID-19 tweets dataset for our analysis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. We also examined the data within the healthcare disparity lens to find topics related to pregnancy, maternal care, and COVID-19.\u003c/p\u003e \u003cp\u003eIn summary, the main research objectives of this article are as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eUtilize the NLP algorithm to evaluate patient experience related to maternal health using social media data\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eClassify text data to various topics relevant to maternal health and conduct sentimental analysis\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eWe conducted a literature review to understand how patients\u0026rsquo; experience was captured and what tools and techniques are used to garner the patients\u0026rsquo; experience. We also discuss studies focusing on NLP techniques, specifically topic modeling and sentiment analysis.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research in Patient-centered Care and Patient Experience Improvement\u003c/h2\u003e \u003cp\u003eNumerous healthcare organizations have undertaken patient-centered care as part of their mission and strategy when IOM announced patient-centered care as one of its six objectives for improving healthcare [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Over the past decade, there has been a significant advancement in evaluating patients' experiences, demonstrating the value of incorporating the patients' insights and demands into the healthcare system [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Patient-centered care includes showing respect toward patients\u0026rsquo; values and choices [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], integrating care coordination and improving access to care [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], educating patients on their clinical status, prognosis, and progress throughout their journey [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], providing physical comfort and emotional support [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and ensuring patients care continuation and well-being during and after discharge [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A lack of patient-centered care will result in unmet patient needs, waste of resources, and ineffective care [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Several researchers found that patient-centered care was associated with improved patient outcomes, which include improved quality of life [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the healthcare industry is becoming increasingly patient-centric, a need exists to quantify, record, and improve patients' experience under their care [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Patients' experiences of the care and the feedback extracted from patients about those experiences are integrated to conceptualize patient experience and satisfaction, which are crucial for improving healthcare systems. According to[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Press (2014), incorporating patients' experiences and evaluating the patients' nonclinical needs can improve healthcare systems and reduce malpractice claims. In the US, the federal government entities, the Centers for Medicare and Medicaid Services (CMS) and the Agency for Healthcare and Research Quality (AHRQ), developed a survey named Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to assess the patient satisfaction. The patient satisfaction metrics should consider evaluating the cognitive and emotional aspects of patients' satisfaction that impact the clinical outcomes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Patient experience can be measured using journey mapping, qualitative and quantitative surveys [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], artificial intelligence techniques, technology, and digital tools [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe evaluation of patient experience helps healthcare organizations extract actionable insights to improve healthcare quality [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], increase engagement with the patient and their healthcare provider [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], improve care effectiveness, lower employee turnover, and enhance employee satisfaction [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Studies have also shown that patient experience can impact critical financial levels dependent on patient retention and medical malpractice claims [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research in Text Analytics\u003c/h2\u003e \u003cp\u003eText analytics collect trends, insights, sentiments, and topics of interest using an automated process of drawing information from unstructured data and take advantage of tools, methods, and mathematical algorithms to analyze and make computers understand text and speech [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. It can differentiate between positive and negative emotions from the text [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], discussed topics [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and the association between the keywords. Text analytics contain various applications, such as descriptive, prescriptive, or predictive analytics [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the most significant methodologies of text analytics is Natural Language Processing (NLP). NLP, a branch of Artificial Intelligence (AI), essentially gives computers the power to understand human language from written text and spoken words, similar to how an ordinary person would understand [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. It allows computers to perform a series of processes to disintegrate the human text and comprehend human language by consolidating computational linguistics and machine learning tools [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. For instance, researchers have used NLP in various healthcare applications, including a large-scale analysis of the counseling conversation to provide effective counseling to patients [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], evaluation of health insurance claims to find fraud or abuse [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], assessment and rehabilitation of patients during the COVID-19 pandemic, digitization and classification of prescriptions [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], and extraction of patient information from electronic healthcare records [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], and patients\u0026rsquo; sentiments from healthcare surveys [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. This review discusses topic modeling, sentimental analysis, and n-gram analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2.1 Topic Modeling\u003c/h2\u003e \u003cp\u003eTopic modeling is an effective and practical tool in NLP for analyzing large text documents [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Topic modeling automatically groups words into topics and identifies relationships between documents within a dataset. In other words, it aims to characterize a text (such as articles, social media postings, survey results, and interview responses) as a distribution over topics and the topics as a distribution over words. The number of topics and the words within each topic are measured using a coherence score, and the similarity of these words to each other is measured [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLatent Dirichlet Allocation (LDA) is one of the most widely used topic modeling algorithms [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] to represent a group of documents based on their underlying themes. It is an unsupervised learning method; i.e., the topics are identified without prior knowledge of their content [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. LDA utilizes the coherence score to measure the optimal number of topics that provide the maximum coherence value [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. LDA aims to determine the range of issues in a specific document. LDA has been applied to a wide range of NLP tasks, which include topic classification from journals and newsgroups [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. LDA has also been widely used in the medical and healthcare sectors to analyze and classify various types of healthcare data. Applications of LDA in healthcare include analyzing electronic health records (EHRs) and patient feedback responses to prioritize patient experience improvement initiatives. LDA can analyze EHRs that contain a wealth of information, including clinical notes, diagnosis codes, and laboratory results, to identify patterns in patient data, such as comorbidities, medication use, side effects, disease progression, and treatment outcomes [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Several researchers used LDA to analyze patient feedback from hospital surveys and online reviews to identify factors influencing patient satisfaction. They found that topics such as communication, nursing care, staff attitude, care quality, waiting time, facility quality, and overall experience impact patient reviews of the hospital [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Improvements to various systems, such as appointment scheduling and billing, are crucial for improving the patients' experience.\u003c/p\u003e \u003cp\u003eThe use of social media analysis in evaluating patient experience utilizes LDA techniques. [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] Okon et al. (2020) used LDA to analyze over 176,000 Reddit threads that provided feedback about dermatology patients' experiences. Ortega (2021) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] utilized patient feedback from social media (Twitter and Reddit) and applied LDA to evaluate breast cancer patients\u0026rsquo; experiences, find the latent topics shared by the patients, and evaluate the sentiments behind those topics. In addition to identifying topics related to patient experience, LDA can also be used to analyze changes in patient experience over time. Ao et al. (2020) [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]used LDA to analyze patient feedback over three years to identify changes in patient experience. They found that while topics related to communication and staff attitude remained consistent, topics related to waiting time and access to care became more prominent in later years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2.2 Sentiment Analysis\u003c/h2\u003e \u003cp\u003eOne of the most widely used tools to garner sentiment from text or voice messages is Sentiment Analysis, which classifies the underlying emotion as positive, negative, and neutral [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. We discuss three sentimental analysis methods: machine learning algorithms, rule-based systems, and lexicon-based approaches. A Lexicon-based approach uses dictionaries of sentiment-laden words to classify text based on the presence or absence of particular words or phrases and utilize the term phrases, sentimental idioms, and expressions. On the other hand, a machine learning-based approach uses a computer model to train an existing dataset with defined emotions and use those trained models to predict the sentiment of new text data [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. The rule-based systems use a set of predefined rules to classify text based on specific patterns.\u003c/p\u003e \u003cp\u003eThe Valence Aware Dictionary and sEntiment Reasoner (VADER) is a sentiment analysis tool based on the rule-based system specifically designed to handle social media data, which often contains informal language (often used in social media texts), slang, and abbreviated words. It has gained significant attention recently due to its high accuracy and efficiency in analyzing sentiment in social media texts [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. They showed that VADER\u0026rsquo;s accuracy in identifying neutral sentiments outperformed other sentiment analysis tools, such as TextBlob and the Stanford CoreNLP. Elbagir and Yang (2019) [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]used VADER and NLTK sentiment analysis tools to interpret sentiments in Twitter data. Here also, VADER achieved higher accuracy and was more capable of handling contextual information than NLTK. A. Kumar et al. (2020) [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e] showed that VADER performed better than SentiStrength and AFINN in analyzing sentiments, irony, and sarcasm in online product reviews. Our study used the VADER sentiment analysis to analyze maternal patient experience from Twitter.\u003c/p\u003e \u003cp\u003eDifferent industries, such as tourism, politics, and marketing, extensively utilize the power of sentiment analysis [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e] to discern and extract subjective insights from customer reviews and utilize those insights to improve their service [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Sentiment analysis is widely used in clinical research and health informatics to analyze patients\u0026rsquo; sentiments regarding their care and identify areas to improve healthcare quality. Asghar et al. (2016) [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]have reviewed several use cases for sentimental analysis in healthcare.\u003c/p\u003e \u003cp\u003eSentiment analysis gives decision-makers insights into how patients feel toward caregivers and treatment systems [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. Greaves et al. (2014) [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e] presented a mixed-method study to evaluate the patient experience and hospital quality from a small number of tweets. Hawkins et al. (2016) [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e] utilized a machine learning approach to analyze data from 2,349 US hospitals over one year to determine the patient's experience, including their care, experience from hospital administration, and interaction with healthcare professionals. Crannell et al. (2016)[\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e] presented a study that analyzed emotions from tweets of various cancer patients for unique cancer diagnostics. Similarly, Rodrigues et al. (2016) [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]have introduced a tool called SentiHealth-Cancer (SHC-pt) to identify the mental condition of cancer patients from social media. While surveys and patient feedback are commonly used to measure patient experience, patient journey mapping can capture it more comprehensively. Ortega (2021) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] utilized sentimental analysis to provide valuable insights for improving empathetic and respectful care in clinical systems and enhancing patient-centered care.\u003c/p\u003e \u003cp\u003ePolarity and subjectivity are the two most popular measures of sentiment analysis [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e], [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e], [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. Subjective texts often have more complex and nuanced meanings than objective ones [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e], [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. For instance, a positive review of a restaurant may contain various subjective expressions such as \"the food was amazing,\" \"the ambiance was fantastic,\" or \"the service was outstanding.\" Subjectivity in sentiment analysis refers to the extent to which a text expresses personal opinions, feelings, or attitudes [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. Polarity scores help to identify the text's mood (positive, neutral, or negative sentiments). There are different approaches to detecting subjectivity and polarity in sentiment analysis. One common practice is to use lexicons or dictionaries that contain words with positive or negative connotations. This method involves assigning a sentiment score to each word in the text based on its polarity and then combining these scores to obtain an overall sentiment score. Another approach is to use machine learning algorithms, such as support vector machines or neural networks, that are trained on a dataset of annotated texts to predict the sentiments of new texts.\u003c/p\u003e \u003cp\u003eAfter the sentiment analysis, the N-gram analysis was conducted for every topic depending on the polarity. N-gram analysis is a text mining technique used to analyze the structure and content of written language [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e], [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e], [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. An N-gram is a contiguous sequence of n items from a given text sample, where n is an integer representing the number of items in the sequence [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the literature review based on the tools, techniques, and applications for sentimental analysis and LDA. From the discussion of the relevant literature, we find that there needs to be a research gap in evaluating efficient patient experience using social media analytics. Although several studies focused on data collected from social media, they always needed a more efficient and effective way of sentiment analysis.\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\u003eSummary of Literature Review\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePublications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSentiment Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTopic modeling using Latent Dirichlet Allocation (LDA)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eML approach\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLexicon based approach\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOnline reviews or social media\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElectronic health record\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatra et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFairie et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHao et al. (2017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJi et al. (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrtega, (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOkon et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChintalapudi et al., 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreaves et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrannell et al., 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRodrigues et al. (2016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMouthami et al., 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElbagir \u0026amp; Yang, (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsghar et al. (2016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKumar et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study aims to evaluate the maternal patient experience during the COVID-19 pandemic from social media. To achieve this objective, we formulated an NLP algorithm to discover the dominant topics patients express on Twitter and the sentiment behind them and measure patient experience.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methodology","content":"\u003cp\u003eTo evaluate the patient's experience from Twitter, the study follows a properly defined series of steps to extract the common topics maternal patients talk about and then assess the sentiment behind them. We used Python 3.5 in Jupyter Notebook and relevant packages for our analysis. At first, data preprocessing was done to make the data ready for Natural Language Processing algorithms.\u003c/p\u003e \u003cp\u003eIn this study, we utilized the Tweets published in an IEEE source [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] that were collected using several COVID-19-related keywords. A subset of the dataset of 28,087,954 tweets was used in this study. These tweets are specifically from the users who posted regarding the COVID-19 pandemic. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a high-level overview of the study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Preprocessing\u003c/h2\u003e \u003cp\u003eFirst, the tweets are preprocessed using R programming, which filters out the tweets posted only from the USA. Several R packages, such as lubridate, dplyr, plyr, and tidyr, were used. Then we used several relevant keywords identified for maternal patients [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e], [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e] such as 'maternal', 'nursing', 'maternal_health', 'pregnancy', 'preeclampsia','pre-eclampsia', 'infant', 'motherhood', 'gynecology', 'postpartum', 'maternalmentalhealth', 'maternal_mortality', 'obstetrical', 'womenshealth', 'doula','obstet', 'pregnancyrelated', 'gynecology', 'cesarean', 'preterm', 'pregnancyrelated', 'gynecol', 'perinatal', 'blackmaternalhealth', 'childbirth', 'pregnant', 'mentalhealth', 'breastfeeding', 'momlife', 'birth', 'baby', 'blackmamasmatter', 'healthcare', 'newmom', 'newborn', 'fourthtrimester', 'maternalhealthmatters', 'birthworker', 'postnatal', 'postpartumjourney', 'midwife', 'maternitycare', 'midwives', 'mother', 'holisticpregnancy', 'maternal', 'breastfeedingmom', 'reclaimlabor', 'charlestonsc', 'healthypregnancy', 'educateyourself', 'reclaimbirth', 'postpartumsupport', 'informeddecisions', 'intentionalbirth', 'reclaimourbodies', 'perinatalmentalhealth', 'birthinpower', 'birthsupport', 'selfcare', 'antenatal', 'antenatal care' were used to further filter out the tweets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 NLP Pipeline\u003c/h2\u003e \u003cp\u003eAfter preprocessing the dataset, selected tweets were isolated and fed into the NLP pipeline. There are several steps for the NLP, which are described in a flow chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe further processing uses the NLTK package to remove unnecessary stop words, emoji, and punctuations from the raw tweets that do not convey any meaning for the topic modeling. Then, the words are tokenized, and then lemmatization is done using Python's NLTK library stemming. In lemmatization, words are converted into the base form for efficient analysis. This is useful for normalizing words and reducing inflected forms to a common base, thus aiding in text analysis and comparison. For instance, if the original word is “running” after lemmatization, it would be “run.” Each word was tagged with its respective part of speech. Parts of speech tagging are fundamental tasks in natural language processing (NLP). It involves assigning each word in a sentence with its corresponding grammatical category or part of speech (e.g., noun, verb, adjective, etc.). This process helps to extract meaningful information and understand the syntactic structure of a sentence. After this step, the dataset is ready for the Topic Modeling algorithm.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3.2.1 Topic modeling\u003c/h3\u003e\n\u003cp\u003eSince the inputs to the topic modeling are tweets, the model characterizes tweets as a distribution over topics and topics as a distribution over words. Essentially, this means that a tweet is assigned a probability for each topic, and each topic is assigned a probability for each word. We used LDA to determine the likelihood of a given string, whether a sentence or a document, using the likelihood of the string within the domain. Blei et al. (2003) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]explained the following generative process for LDA:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRandomly select a distribution over topics for every tweet\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor every word in the tweet:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRandomly select a topic from a distribution over topics in step 1\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFrom the corresponding distribution over the vocabulary, randomly select a word\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIn this study, LDA was implemented using Python’s genism package. Python's NLTK and genism packages used processed tweets to identify the specified topics. We select the optimal number of topics based on the coherence score for each topic that provides the maximum coherence value. An inter-topic distribution mapping is used to visualize the topics.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2.2 Sentiment Analysis\u003c/h2\u003e \u003cp\u003eWe reiterate that the primary objective of sentiment analysis is to categorize a specific sentence or block of text as positive, negative, or neutral. We used TextBlob and VaderSentiment packages of Python to perform sentimental analysis on the Twitter data after we classified them into specific dominant topics. We used polarity and subjectivity to quantify sentiment analysis [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e], [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e], [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe used the VADER tool for analyzing sentiments that follow a set of rules. The analysis classifies the text's tone as positive, negative, or neutral. The overall sentiment score produced by VADER is a continuous value that ranges from − 1 to 1, where − 1 indicates highly negative sentiment, 0 indicates neutral sentiment, and 1 shows extremely positive sentiment. VADER also generates scores for the three sentiment categories (positive, negative, and neutral) and a compound score. This normalized weighted composite score represents the overall sentiment of the text on a scale from − 1 to 1. In this study, we utilized the lexicon-based approach to quantify the subjectivity of every tweet using Python’s TextBlob package. After the sentiment analysis, the final output contains every tweet classified into a topic along with the sentiment behind that topic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2.3 N-gram Analysis\u003c/h2\u003e \u003cp\u003eThe sample text is divided into N-grams in N-gram analysis, which are then counted and analyzed to determine their frequency and distribution. The most common form of N-gram analysis is the bigram (n = 2), which considers pairs of adjacent words in the text. This type of analysis helps identify patterns and relationships between words in the text, such as common collocations or idiomatic expressions. Trigram (n = 3) analysis considers three adjacent words, and higher-order N-grams consider even more words. N-gram analysis is widely used in various applications, such as natural language processing, information retrieval, and machine learning. For instance, it can be used to identify the most frequently occurring words in a given text, to identify patterns in the usage of certain words or phrases, or to develop language models that can predict the next word in a sentence based on the preceding N-grams.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cp\u003eAt the first stage of data preprocessing from 28\u0026nbsp;million tweets, around 31,438 tweets were extracted about maternal healthcare and from the USA. After the stop word removal and lemmatization, the processed tweets were transferred to the next step, topic modeling.\u003c/p\u003e\n\u003cp\u003eFrom the LDA topic evaluation that uses different values of \u0026lsquo;number of topics,\u0026rsquo; we find the optimal number of topics to be three. The LDA model coherence values are shown below in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. In this figure, we can see that for the number of topics 3, the model generated the maximum coherence score of 0.3466. After that, the coherence score decreases as the number of topics increases.\u003c/p\u003e\n\u003cp\u003eHence, the number of topics for the LDA model was set to three, which are visualized in different ways. At first, a word cloud was drawn for every topic depicted in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The keywords inside these word clouds represent each topic.\u003c/p\u003e\n\u003cp\u003eTherefore, the frequency of the most represented keywords in each topic is depicted in the following bar charts in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. It is evident that in Topic 1, \u0026ldquo;fever, baby, mother, birth\u0026rdquo; are most frequent. In contrast, Topic 2 has \u0026ldquo;healthcare, high, help, worker, public, health medical\u0026rdquo; most frequently. In Topic 3, \u0026ldquo;coronavirus, positive, flu, birth, pregnant, test\u0026rdquo; are most frequent.\u003c/p\u003e\n\u003cp\u003eFrom the above analysis, labels can be assigned to every topic. Depending on the keyword frequency in Topic 1, users talking about pregnant mothers and newborn baby\u0026rsquo;s mothers are worried about fever; in Topic 2, impact on healthcare facilities and workers and consequences on expectant mothers; and in Topic 3, tweets are concerned about the rising flu, which could be coronavirus-positive cases, COVID-19 testing, the rapidly spreading virus, and its effect on pregnant mothers.\u003c/p\u003e\n\u003cp\u003eDepending on these keyword frequencies, every tweet is classified into different topics, shown in three colors in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. Here, orange, green, and blue tweets represent three topics based on their keywords. Blue represents topic 1, orange corresponds to topic 2, and green represents topic 3.\u003c/p\u003e\n\u003cp\u003eNext, the distribution of each topic in the dataset is shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. Here, it is evident that most of the tweets fall under Topic 2 and Topic 1.\u003c/p\u003e\n\u003cp\u003eFurthermore, all the dominant topics were shown in an inter-topic distance map to visualize better how topics are classified. This is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, the three topics were again visualized using t-SNE, shown in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e. Each dot represents a tweet, and the three colors indicate different topics. In the t-sne graph, the n-dimensional data is described in two-dimensional space. It is clear from the figure that there is little overlapping between topics.\u003c/p\u003e\n\u003cp\u003eAfter classifying the tweets, the sentiment behind each tweet was measured concerning polarity and subjectivity, as in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The value of polarity ranges from \u0026minus;\u0026thinsp;1 to 1. The value of -1 represents highly negative experience, 1 represents good experience, and 0 represents neutral experience. For subjectivity, the value ranges from 0 to 1. If a tweet contains a more significant amount of personal opinion, the subjectivity value would be close to 1. The value would be close to 0 if it contains factual information.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSentiment Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDominant Topic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTopic_Perc_Contrib\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eActual tweet\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolarity Vader\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003esubjectivity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e@BMaienschein\u003c/p\u003e\n \u003cp\u003eThank you for seeing San Diego\u0026apos;s ME/CFS patients and our many community volunteers and supporters. At this point, we have donated 1620 masks, caps, and other PPE mostly to San Diego healthcare workers and organizations. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://t.co/CwaTq2TqwW\u003c/span\u003e\u003c/span\u003e #MECFSSD #aCure4ME \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://t.co/6k54mUBPwr\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Texas doctors discuss possible pandemic baby boom: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://t.co/00LA8Ny7Tu\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://t.co/sTDYNLqwWJ\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e@realDonaldTrump @CDCgov Covid kills! This is not fake news people are dying. My brother lost his wife to Covid, mother of 7. Really Trump...\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCan\u0026acirc;\u0026euro;\u0026trade;t wait for covid to be over. My baby wanna go to NYC so bad I can\u0026acirc;\u0026euro;\u0026trade;t wait to get her there\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e below, the mean polarity and subjectivity values are calculated. The mean polarity of Topics 1 and 3 conveys negative emotion, and the other topic has fairly positive emotion. However, as the negative and positive polarity tweets negated each other, each topic must be studied separately according to sentiments.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAverage polarity and subjectivity depending on topics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDominant topic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage polarity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage subjectivity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.003856237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3548346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065616866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3669842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.199848470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3639693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFirst, sorting the dataset for only Topic 1 with negative polarity, which essentially means negative sentiment, and running an N-gram analysis presents the following, as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The N-gram analysis in Table\u0026nbsp;4 shows that mothers worry about their babies catching a fever and how pregnant mothers are severely affected by COVID-19. On the other hand, tweets on the same topic that express positive sentiment mostly talk about how pregnant mothers are surviving the coronavirus. Even during the COVID pandemic, they are optimistic about safe childbirth and express hope for healthy newborns. The N-gram analysis of that portion is shown in Table\u0026nbsp;5.\u003c/p\u003e\n\u003cdiv\u003eIn the case of Topic 2, tweets that convey negative emotion talk mainly about how healthcare providers are having a tough time due to the risks and a considerable number of patients, as shown in Table 6. However, the tweets with positive sentiment talk about helping healthcare agencies tackle the spread of the virus, as shown in Table 7.\u003c/div\u003e\n\u003cp\u003eIf focused on the N-gram analysis of the negative emotions\u0026rsquo; tweets of Topic 3, we found that the mothers are worried about their babies testing positive for coronavirus, as shown in Table 8. On the contrary, the positive tweets of Topic 3 usually talk about how mothers are getting care from healthcare workers, and the prevention strategy of widespread testing taken by the healthcare facilities is depicted in Table 9.\u003c/p\u003e\n\u003ch2\u003e4.2 Preliminary Analysis of Disparity\u003c/h2\u003e\n\u003cp\u003eWe also utilized this study to conduct a preliminary analysis of the use of social media analysis to capture healthcare disparities. Upon careful examination of the tweets with a specified list of keywords regarding healthcare inequality, we found that most tweets focused mainly on racial disparities in healthcare. A word cloud is depicted in Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e to show the racial disparity-related tweets. Topics such as minority, race, and distrust are frequent in these tweets.\u003c/p\u003e\n\u003cp\u003eAn N-gram analysis of the following tweets found that African-American mothers are worried about not getting proper healthcare services. The N-gram analysis is shown in Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSome of the actual tweets posted by the users are depicted in Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e. These representative tweets say a great deal about the racial injustice in healthcare in the USA, which was especially aggravated during the COVID-19 pandemic.\u003c/p\u003e\n\u003ch2\u003e4.3 Limitations of This Study\u003c/h2\u003e\n\u003cp\u003eHowever, there are a few limitations associated with analyzing Twitter data. For instance, users often use non-standard text, incorrect English, multilingual content, and emojis to express their opinions, which makes it incredibly challenging for NLP to classify some of the tweets and calculate the sentiment accurately. Also, users use sarcasm to express their views, which is very complex for the program to understand. There are also spelling mistakes or incomplete sentences, which makes it difficult for the model to understand the underlying sentiment. It is essential to preprocess the data to ensure that these algorithms provide valuable information. However, computers may not be able to distinguish between similar words such as \u0026quot;doctor\u0026quot; and \u0026quot;physician\u0026quot; or identify that \u0026quot;meal\u0026quot; and \u0026quot;meals\u0026quot; convey the same information. Additionally, spelling errors may create further complications because a minor modification in a word can make the computer perceive it as entirely different. These constraints are solved by using stemming or lemmatizing to reduce words to their base form, utilizing NLP libraries to correct spelling errors, manually analyzing comments to identify context-specific words, and replacing them with common words. Preprocessing patient-derived data may be more complex because respondents may need more healthcare literacy, make spelling mistakes, or use texting language outside the NLP library used for processing. Besides, sometimes tweets contain phrases that need to convey the words\u0026apos; literal meaning, making it difficult for the program to understand. For instance, users who use the phrase \u0026lsquo;baby fever\u0026rsquo; usually express the longing that some people experience that relates to their desire to have a child. Additionally, isolating unique tweets from millions of tweets is challenging as there are several occasions of retweeting or quoting another person\u0026apos;s tweet. However, the computer program interprets the literal meaning of the phrase, so the program fails to extract the proper sentiment from tweets. Nevertheless, despite all these limitations, the model produces an initial insight that gives policymakers a way to capture patient experience to improve healthcare systems and understand system-level concerns for the under-represented communities.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we have analyzed the tweets to extract maternal patient experience and classified the different topics patients are talking about and the sentiment behind each of them. As we can see, topics have distinct levels of sentiment associated with them, which shows the diversity of patient experience. Another aspect to note is that on social media, patients and their relatives post their experiences, giving various perspectives on medical care. The observed result from the LDA model classifies tweets into different topics, which helps to narrow down the patient's experience. The sentiment analysis also provides meaningful information regarding the maternal patient experience. Moreover, through the N-gram analysis, we delivered what is discussed in each topic and critical insights regarding racial inequity in healthcare systems.\u003c/p\u003e\u003cp\u003eThe analysis of this study would work as a guideline for policymakers inside a healthcare system. As among the developed countries in the world, the US has the highest rate of maternal mortality or morbidity; analysis of patient experience can provide valuable insights regarding patients' expectations of care and the original quality of care that they received. Utilizing the topics identified using topic modeling, policymakers, and community organizations can understand the patients' negative and positive experiences and develop policies accordingly. Moreover, social media analytics provides an initial insight into the healthcare disparities from the system-level point of view that should be captured in patient experience surveys or other tools. One recommendation for future work is to include other social media data and data from different languages. Because there are many multilingual users in social media, the model would be able to capture more information. Another important addition can be using Process mining techniques to explore the patient journey map and take necessary action to improve the quality of care. Suppose policymakers have access to the surveys of the patients’ experience collected through interviews. In that case, this data can be incorporated into the model to obtain much more insights that improve healthcare quality. Typical conditions such as postpartum blues and physician burnout may have caused the negative patient experience; COVID might have added to the negativity of experiences during these times.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eDeclaration of interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eFunding declaration\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthor contribution declaration\u003c/p\u003e\n\u003cp\u003eDebapriya Banik, Sreenath Chalil Madathil, Amit Joe Lopes, Sergio A. Luna Fong and Santosh K. Mukka\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003econtributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. Wolfe, \u0026ldquo;Institute of Medicine report: crossing the quality chasm: a new health care system for the 21st century,\u0026rdquo; \u003cem\u003ePolicy Polit Nurs Pract\u003c/em\u003e, vol. 2, no. 3, pp. 233\u0026ndash;235, 2001.\u003c/li\u003e\n\u003cli\u003eD. M. Wolf, L. Lehman, R. Quinlin, T. Zullo, and L. Hoffman, \u0026ldquo;Effect of patient-centered care on patient satisfaction and quality of care,\u0026rdquo; \u003cem\u003eJ Nurs Care Qual\u003c/em\u003e, vol. 23, no. 4, pp. 316\u0026ndash;321, 2008.\u003c/li\u003e\n\u003cli\u003eV. J. T. 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Declercq, \u0026ldquo;All-cause maternal mortality in the US before vs during the COVID-19 pandemic,\u0026rdquo; \u003cem\u003eJAMA Netw Open\u003c/em\u003e, vol. 5, no. 6, pp. e2219133\u0026ndash;e2219133, 2022.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 4 To 10","content":"\u003cp\u003eTable 4 To 10 are available in the Supplementary Files section.\u003c/p\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":"Maternal health, patient experience, natural language processing, sentiment analysis, healthcare systems","lastPublishedDoi":"10.21203/rs.3.rs-3881957/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3881957/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe healthcare sector constantly investigates ways to improve patient outcomes and provide more patient-centered care. Delivering quality medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback, such as HCAHPS, to measure patients' experiences. The power of social media can be harnessed using artificial intelligence and machine learning techniques to provide researchers with valuable insights into understanding patient experience and care. Our primary research objective is to develop a social media analytics model to evaluate the maternal patient experience during the COVID-19 pandemic.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eWe used the \"COVID-19 Tweets\" Dataset, which has over 28\u0026nbsp;million tweets, to evaluate patient experience using Natural Language Processing (NLP) and extract tweets from the US with words relevant to maternal patients. The maternal patient cohort was selected because the United States has the highest percentage of maternal mortality and morbidity rate among the developed countries in the world.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe created word clouds, word clustering, frequency analysis, and network analysis of words that relate to \u0026ldquo;pains\u0026rdquo; and \u0026ldquo;gains\u0026rdquo; regarding the maternal patient experience, which are expressed through social media.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis model will help process improvement experts without domain expertise understand various domain challenges efficiently. Such insights can help decision-makers improve the patient care system. We also conducted a preliminary study to discover if a particular group faces racial health inequity.\u003c/p\u003e","manuscriptTitle":"Evaluation of Maternal Patient Experience Through Natural Language Processing Techniques: The Case of Twitter Data in The United States During COVID-19","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 17:06:46","doi":"10.21203/rs.3.rs-3881957/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":"6c444b56-15b1-4ead-8fbf-54b2d5a960b4","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-06T17:06:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-06 17:06:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3881957","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3881957","identity":"rs-3881957","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}