Does Pollyanna hypothesis hold true in death narratives? A sentiment analysis approach.

Pollyanna hypothesis claims that human beings have a universal tendency to use positive words more frequently and broadly than negative words. The present study aims to test Pollyanna hypothesis in medical death narratives at both lexical and text levels by using sentiment analysis and emotion detection methods, and to qualitatively analyze the contextual use of emotion words to deepen the understanding of doctors' emotions. Sentiment analysis showed a strong token-based linguistic positivity and a weak type-based negativity bias at the lexical level, and a general positivity bias at the text level, despite the gender of the doctors. Emotion detection discovered three prominent emotions of "joy", "sadness", and "anger", and a greater diversity of negative emotions in contrast to positive emotions in medical death narratives. Contextual analysis revealed that emotion words associated with joy were primarily observed in contexts related to doctors' actions and behaviors aiming to benefit others and promote social wellbeing. Emotion words associated with sadness and anger were chiefly employed to describe situations involving patients' death and doctors' attitudes towards death. The results confirm Pollyanna hypothesis at both token-based lexical level and text level and falsify the hypothesis at type-based lexical level. Possible explanations are explored by contextual analysis, and theoretical analysis from the perspectives of cognitive linguistics and social psychology. The findings are expected to enrich the understanding of Pollyanna hypothesis as well as the junior doctors' emotional responses to clinical deaths.

Domain adaptive learning for multi realm sentiment classification on big data.

Machine learning techniques that rely on textual features or sentiment lexicons can lead to erroneous sentiment analysis. These techniques are especially vulnerable to domain-related difficulties, especially when dealing in Big data. In addition, labeling is time-consuming and supervised machine learning algorithms often lack labeled data. Transfer learning can help save time and obtain high performance with fewer datasets in this field. To cope this, we used a transfer learning-based Multi-Domain Sentiment Classification (MDSC) technique. We are able to identify the sentiment polarity of text in a target domain that is unlabeled by looking at reviews in a labelled source domain. This research aims to evaluate the impact of domain adaptation and measure the extent to which transfer learning enhances sentiment analysis outcomes. We employed transfer learning models BERT, RoBERTa, ELECTRA, and ULMFiT to improve the performance in sentiment analysis. We analyzed sentiment through various transformer models and compared the performance of LSTM and CNN. The experiments are carried on five publicly available sentiment analysis datasets, namely Hotel Reviews (HR), Movie Reviews (MR), Sentiment140 Tweets (ST), Citation Sentiment Corpus (CSC), and Bioinformatics Citation Corpus (BCC), to adapt multi-target domains. The performance of numerous models employing transfer learning from diverse datasets demonstrating how various factors influence the outputs.

CIDER: Context-sensitive polarity measurement for short-form text.

Researchers commonly perform sentiment analysis on large collections of short texts like tweets, Reddit posts or newspaper headlines that are all focused on a specific topic, theme or event. Usually, general-purpose sentiment analysis methods are used. These perform well on average but miss the variation in meaning that happens across different contexts, for example, the word "active" has a very different intention and valence in the phrase "active lifestyle" versus "active volcano". This work presents a new approach, CIDER (Context Informed Dictionary and sEmantic Reasoner), which performs context-sensitive linguistic analysis, where the valence of sentiment-laden terms is inferred from the whole corpus before being used to score the individual texts. In this paper, we detail the CIDER algorithm and demonstrate that it outperforms state-of-the-art generalist unsupervised sentiment analysis techniques on a large collection of tweets about the weather. CIDER is also applicable to alternative (non-sentiment) linguistic scales. A case study on gender in the UK is presented, with the identification of highly gendered and sentiment-laden days. We have made our implementation of CIDER available as a Python package: https://pypi.org/project/ciderpolarity/.

Sentiment analysis using averaged weighted word vector features.

People use the World Wide Web heavily to share their experiences with entities such as products, services or travel destinations. Texts that provide online feedback through reviews and comments are essential for consumer decisions. These comments create a valuable source that may be used to measure satisfaction related to products or services. Sentiment analysis is the task of identifying opinions expressed in such text fragments. In this work, we develop two methods that combine different types of word vectors to learn and estimate the polarity of reviews. We create average review vectors from word vectors and add weights to these review vectors using word frequencies in positive and negative sensitivity-tagged reviews. We applied the methods to several datasets from different domains used as standard sentiment analysis benchmarks. We ensemble the techniques with each other and existing methods, and we compare them with the approaches in the literature. The results show that the performances of our approaches outperform the state-of-the-art success rates.

Social media-based demographic and sentiment analysis for disaster responses.

This study explores disaster responses across the United States for Winter Storm Jaxon in 2018 by utilizing demographic and sentiment analysis for Twitter®. This study finds that people show highly fluctuated responses across the study periods and highest natural sentiment, followed by positive sentiment and negative sentiment. Also, some sociodemographic and Twitter variables, such as gender and long text, are strongly related to human sentiment, whereas other sociodemographic and Twitter variables, such as age and the higher number of retweets, are not associated with it. The results show that governments and disaster experts should consider a multitude of sociodemographic and Twitter variables to understand human responses and sentiment during natural disaster events.

Patient perceptions of disease burden and treatment of myasthenia gravis based on sentiment analysis of digital conversations.

Myasthenia gravis (MG) is a rare, autoimmune, antibody-mediated, neuromuscular disease. This study analyzed digital conversations about MG to explore unprovoked perspectives. Advanced search, data extraction, and artificial intelligence-powered algorithms were used to harvest, mine, and structure public domain digital conversations about MG from US Internet Protocol addresses (August 2021 to August 2022). Thematic analyses examined topics, mindsets, and sentiments/key drivers via natural language processing and text analytics. Findings were described by sex/gender and treatment experience with steroids or intravenous immunoglobulin (IVIg). The 13,234 conversations were extracted from message boards (51%), social media networks (22%), topical sites (21%), and blogs (6%). Sex/gender was confirmed as female in 5703 and male in 2781 conversations, and treatment experience was with steroids in 3255 and IVIg in 2106 conversations. Topics focused on diagnosis (29%), living with MG (28%), symptoms (24%), and treatment (19%). Within 3176 conversations about symptoms, eye problems (21%), facial muscle problems (18%), and fatigue (18%) were most commonly described. Negative sentiments about MG were expressed in 59% of conversations, with only 2% considered positive. Negative conversations were dominated by themes of impact on life (29%), misdiagnosis problems (27%), treatment issues (24%), and symptom severity (20%). Impact on life was a key driver of negativity in conversations by both men (27%) and women (34%), and treatment issues was a dominant theme in conversations by steroid-treated (29%) and IVIg-treated (31%) patients. Of 1382 conversations discussing treatment barriers, 36% focused on side effects, 33% on lack of efficacy, 21% on misdiagnosis, and 10% on cost/insurance. Side effects formed the main barrier in conversations by both steroid-treated and IVIg-treated patients. Capturing the patient voice via digital conversations reveals a high degree of concern related to burden of disease, misdiagnosis, and common MG treatments among those with MG, pointing to a need for treatment options that can improve quality of life.

Chinese text dual attention network for aspect-level sentiment classification.

English text has a clear and compact subject structure, which makes it easy to find dependency relationships between words. However, Chinese text often conveys information using situational settings, which results in loose sentence structures, and even most Chinese comments and experimental summary texts lack subjects. This makes it challenging to determine the dependency relationship between words in Chinese text, especially in aspect-level sentiment recognition. To solve this problem faced by Chinese text in the field of sentiment recognition, a Chinese text dual attention network for aspect-level sentiment recognition is proposed. First, Chinese syntactic dependency is proposed, and sentiment dictionary is introduced to quickly and accurately extract aspect-level sentiment words, opinion extraction and classification of sentimental trends in text. Additionally, in order to extract context-level features, the CNN-BILSTM model and position coding are also introduced. Finally, to better extract fine-grained aspect-level sentiment, a two-level attention mechanism is used. Compared with ten advanced baseline models, the model's capabilities are being further optimized for better performance, with Accuracy of 0.9180, 0.9080 and 0.8380 respectively. This method is being demonstrated by a vast array of experiments to achieve higher performance in aspect-level sentiment recognition in less time, and ablation experiments demonstrate the importance of each module of the model.

Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study.

BACKGROUND: Social media has the potential to be of great value in understanding patterns in public health using large-scale analysis approaches (eg, data science and natural language processing [NLP]), 2 of which have been used in public health: sentiment analysis and topic modeling; however, their use in the area of food security and public health nutrition is limited. OBJECTIVE: This study aims to explore the potential use of NLP tools to gather insights from real-world social media data on the public health issue of food security. METHODS: A search strategy for obtaining tweets was developed using food security terms. Tweets were collected using the Twitter application programming interface from January 1, 2019, to December 31, 2021, filtered for Australia-based users only. Sentiment analysis of the tweets was performed using the Valence Aware Dictionary and Sentiment Reasoner. Topic modeling exploring the content of tweets was conducted using latent Dirichlet allocation with BigML (BigML, Inc). Sentiment, topic, and engagement (the sum of likes, retweets, quotations, and replies) were compared across years. RESULTS: In total, 38,070 tweets were collected from 14,880 Twitter users. Overall, the sentiment when discussing food security was positive, although this varied across the 3 years. Positive sentiment remained higher during the COVID-19 lockdown periods in Australia. The topic model contained 10 topics (in order from highest to lowest probability in the data set): "Global production," "Food insecurity and health," "Use of food banks," "Giving to food banks," "Family poverty," "Food relief provision," "Global food insecurity," "Climate change," "Australian food insecurity," and "Human rights." The topic "Giving to food banks," which focused on support and donation, had the highest proportion of positive sentiment, and "Global food insecurity," which covered food insecurity prevalence worldwide, had the highest proportion of negative sentiment. When compared with news, there were some events, such as COVID-19 support payment introduction and bushfires across Australia, that were associated with high periods of positive or negative sentiment. Topics related to food insecurity prevalence, poverty, and food relief in Australia were not consistently more prominent during the COVID-19 pandemic than before the pandemic. Negative tweets received substantially higher engagement across 2019 and 2020. There was no clear relationship between topics that were more likely to be positive or negative and have higher or lower engagement, indicating that the identified topics are discrete issues. CONCLUSIONS: In this study, we demonstrated the potential use of sentiment analysis and topic modeling to explore evolution in conversations on food security using social media data. Future use of NLP in food security requires the context of and interpretation by public health experts and the use of broader data sets, with the potential to track dimensions or events related to food security to inform evidence-based decision-making in this area.

Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022.

BACKGROUND: As the impact of the COVID-19 pandemic winds down, both individuals and society are gradually returning to life and activities before the pandemic. This study aims to explore how people's emotions have changed from the pre-pandemic period during the pandemic to this post-emergency period and whether the sentiment level nowadays has returned to the pre-pandemic level. METHOD: We collected Reddit social media data in 2019 (pre-pandemic), 2020 (peak period of the pandemic), 2021, and 2022 (late stages of the pandemic, transitioning period to the post-emergency period) from the subreddits communities in 128 universities/colleges in the U.S., and a set of school-level baseline characteristics such as location, enrollment, graduation rate, selectivity, etc. We predicted two sets of sentiments from a pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) and a graph attention network (GAT) that leverages both the rich semantic information and the relational information among posted messages and then applied model stacking to obtain the final sentiment classification. After obtaining the sentiment label for each message, we employed a generalized linear mixed-effects model to estimate the temporal trend in sentiment from 2019 to 2022 and how the school-level factors may affect the sentiment. RESULTS: Compared to the year 2019, the odds of negative sentiment in years 2020, 2021, and 2022 are 25%. 7.3%, and 6.3% higher, respectively, which are all statistically significant at the 5% significance level based on the multiplicity-adjusted p-values. CONCLUSIONS: Our study findings suggest a partial recovery in the sentiment composition (negative vs. non-negative) in the post-pandemic-emergency era. The results align with common expectations and provide a detailed quantification of how sentiments have evolved from 2019 to 2022 in the sub-population represented by the sample examined in this study.

Evaluation of bias and gender/racial concordance based on sentiment analysis of narrative evaluations of clinical clerkships using natural language processing.

There is increasing interest in understanding potential bias in medical education. We used natural language processing (NLP) to evaluate potential bias in clinical clerkship evaluations. Data from medical evaluations and administrative databases for medical students enrolled in third-year clinical clerkship rotations across two academic years. We collected demographic information of students and faculty evaluators to determine gender/racial concordance (i.e., whether the student and faculty identified with the same demographic). We used a multinomial log-linear model for final clerkship grades, using predictors such as numerical evaluation scores, gender/racial concordance, and sentiment scores of narrative evaluations using the SentimentIntensityAnalyzer tool in Python. 2037 evaluations from 198 students were analyzed. Statistical significance was defined as P < 0.05. Sentiment scores for evaluations did not vary significantly by student gender, race, or ethnicity (P = 0.88, 0.64, and 0.06, respectively). Word choices were similar across faculty and student demographic groups. Modeling showed narrative evaluation sentiment scores were not predictive of an honors grade (odds ratio [OR] 1.23, P = 0.58). Numerical evaluation average (OR 1.45, P < 0.001) and gender concordance between faculty and student (OR 1.32, P = 0.049) were significant predictors of receiving honors. The lack of disparities in narrative text in our study contrasts with prior findings from other institutions. Ongoing efforts include comparative analyses with other institutions to understand what institutional factors may contribute to bias. NLP enables a systematic approach for investigating bias. The insights gained from the lack of association between word choices, sentiment scores, and final grades show potential opportunities to improve feedback processes for students.

Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model.

Introduction: To protect citizens during the COVID-19 pandemic unprecedented public health restrictions were imposed on everyday life in the UK and around the world. In emergencies like COVID-19, it is crucial for policymakers to be able to gauge the public response and sentiment to such measures in almost real-time and establish best practices for the use of social media for emergency response. Methods: In this study, we explored Twitter as a data source for assessing public reaction to the pandemic. We conducted an analysis of sentiment by topic using 25 million UK tweets, collected from 26th May 2020 to 8th March 2021. We combined an innovative combination of sentiment analysis via a recurrent neural network and topic clustering through an embedded topic model. Results: The results demonstrated interpretable per-topic sentiment signals across time and geography in the UK that could be tied to specific public health and policy events during the pandemic. Unique to this investigation is the juxtaposition of derived sentiment trends against behavioral surveys conducted by the UK Office for National Statistics, providing a robust gauge of the public mood concurrent with policy announcements. Discussion: While much of the existing research focused on specific questions or new techniques, we developed a comprehensive framework for the assessment of public response by policymakers for COVID-19 and generalizable for future emergencies. The emergent methodology not only elucidates the public's stance on COVID-19 policies but also establishes a generalizable framework for public policymakers to monitor and assess the buy-in and acceptance of their policies almost in real-time. Further, the proposed approach is generalizable as a tool for policymakers and could be applied to further subjects of political and public interest.

Sentiment analysis of the COVID-19 vaccine perception.

The sharp rise in coronavirus cases in the United States, as well as other countries, is driven by variants such as the Omicron substrain, BA4 and BA5. Keeping up to date with COVID-19 vaccination and wearing masks are essential tools for mitigating the pandemic. Social media plays a vital role in sharing and exchanging information, but it also affects perceptions of social phenomena. In this study, we conducted sentiment analysis and topic modeling to investigate vaccine perception using 338,465 COVID-19 vaccine-related comments collected from January 2020 to May 2021 on Reddit. This study stands apart from prior COVID-related research on social media, particularly on Reddit, as it conducted separate analyses for each COVID vaccine and examines public sentiment with various societal events, including vaccine development progress and government responses to COVID. The findings reveal two notable spikes in the number of comments containing the keyword "vaccine". This suggests that discussions about vaccines tend to increase during times of significant social and political events, indicating that people's attention and interest in the topic are influenced by current events. Understanding the public perception of vaccines and identifying factors influencing vaccine perception could help propose appropriate interventions to promote vaccination.

A new word embedding model integrated with medical knowledge for deep learning-based sentiment classification.

The development of intelligent systems that use social media data for decision-making processes in numerous domains such as politics, business, marketing, and finance, has been made possible by the popularity of social media platforms. However, the utilization of textual data from social media in the healthcare management industry is still somewhat limited when it is compared to other industries. Investigating how current machine learning and natural language processing technologies can be used in the healthcare industry to gauge public sentiment is an important study. Earlier works on healthcare sentiment analysis have utilized traditional word embedding models trained on the general and medical corpus. However, integration of medical knowledge to pre-trained word embedding models has not been considered yet. Word embedding models trained on the general corpus led to the problem of lacking medical knowledge and the models trained on the small size of the medical corpus have limitations in capturing semantic and syntactic properties. This research proposes a new word embedding model named Word Embedding Integrated with Medical Knowledge Vector (WE-iMKVec). The proposed model integrates sentiment lexicons and medical knowledgebases into the pre-trained word embedding to enrich the properties of word embedding. A new medical-aware sentiment polarity score is proposed for the utilization in learning neural-network sentiment and these vectors incorporate with the original pre-trained word vectors. The resulting vectors are enriched with lexicon vectors and the medical knowledge vectors: Adverse Drug Reaction (ADR) vector and Unified Medical Language System (UMLS) vector are used to build the proposed WE-iMKVec model. WE-iMKVec is validated on the five different social media healthcare review datasets and the empirical results showed its superiority over traditional word embedding models in medical sentiment analysis. The highest improvement can be found in the patients.info medical condition dataset where the proposed model outperforms three conventional word2vec models (Google-News, PubMed-PMC, and Drug Reviews) by 12.7 %, 31.4 %, and 25.4 % respectively in terms of F1 score.

Enhancing machine learning-based sentiment analysis through feature extraction techniques.

A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model's performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.

How satisfied are patients with nursing care and why? A comprehensive study based on social media and opinion mining.

To assess the overall experience of a patient in a hospital, many factors must be analyzed; nonetheless, one of the key aspects is the performance of nurses as they closely interact with patients on many occasions. Nurses carry out many tasks that could be assessed to understand the patient's satisfaction and consequently, the effectiveness of the offered services. To assess their performance, traditionally, expensive, and time-consuming methods such as questionnaires and interviews have been used; nevertheless, the development of social networks has allowed the patients to convey their opinions in a free and public manner. For that reason, in this study, a comprehensive analysis has been performed based on patients' opinions collected from a feedback platform for health and care services, to discover the topics about nurses the patients are more interested in. To do so, a topic modeling technique has been proposed. After this, sentiment analysis has been applied to classify the topics as satisfactory or unsatisfactory. Finally, the results have been compared with what the patients think about doctors. The results highlight what topics are most relevant to assess the patient satisfaction and to what extent. The results remark that the opinion about nurses is, in general, more positive than about doctors.

Areas of interest and sentiment analysis towards second generation antipsychotics, lithium and mood stabilizing anticonvulsants: Unsupervised analysis using Twitter.

BACKGROUND: Severe mental disorders like Schizophrenia and related psychotic disorders (SRD) or Bipolar Disorder (BD) require pharmacological treatment for relapse prevention and quality of life improvement. Yet, treatment adherence is a challenge, partly due to patients' attitudes and beliefs towards their medication. Social media listening offers insights into patient experiences and preferences, particularly in severe mental disorders. METHODS: All tweets posted between 2008 and 2022 mentioning the names of the main drugs used in SRD and BD were analyzed using advanced artificial intelligence techniques such as machine learning, and deep learning, along with natural language processing. RESULTS: In this 15-year study analyzing 893,289 tweets, second generation antipsychotics received more mentions in English tweets, whereas mood stabilizers received more tweets in Spanish. English tweets about economic and legal aspects displayed negative emotions, while Spanish tweets seeking advice showed surprise. Moreover, a recurring theme in Spanish tweets was the shortage of medications, evoking feelings of anger among users. LIMITATIONS: This study's analysis of Twitter data, while insightful, may not fully capture the nuances of discussions due to the platform's brevity. Additionally, the wide therapeutic use of the studied drugs, complicates the isolation of disorder-specific discourse. Only English and Spanish tweets were examined, limiting the cultural breadth of the findings. CONCLUSION: This study emphasizes the importance of social media research in understanding user perceptions of SRD and BD treatments. The results provide valuable insights for clinicians when considering how patients and the general public view and communicate about these treatments in the digital environment.

Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media.

BACKGROUND: The COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic. OBJECTIVE: Mining and analyzing social media text information can not only reflect the changes in public sentiment characteristics during the COVID-19 pandemic but also help the government understand the trends in public opinion and reasonably control public opinion. METHODS: First, this study collected microblog comments related to the COVID-19 pandemic as a data set. Second, sentiment analysis was carried out based on the topic modeling method combining latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). Finally, a machine learning linear regression (ML-LR) model combined with a sparse matrix was proposed to explore the evolutionary trend in public opinion on social media and verify the high accuracy of the model. RESULTS: The experimental results show that, in different stages, the characteristics of public emotion are different, and the overall trend is from negative to positive. CONCLUSIONS: The proposed method can effectively reflect the characteristics of the different times and space of public opinion. The results provide theoretical support and practical reference in response to public health and safety events.

Depression detection for twitter users using sentiment analysis in English and Arabic tweets.

Since depression often results in suicidal thoughts and leaves a person severely disabled daily, there is an elevated risk of premature mortality due to mental problems caused by depression. Therefore, it's crucial to identify the patient's mental illness as soon as possible. People are increasingly using social media platforms to express their opinions and share daily activities, which makes online platforms rich sources of early depression detection. The contribution of this paper is multifold. First, it presents five machine-learning models for Arabic and English depression detection using Twitter text. The best model for Arabic text achieved an f1-score of 96.6 % for binary classification to depressed and Non_dep. For English text without negation, the model achieved 92 % for binary classification and 88 % for multi-classification (depressed, indifferent, happy). For English text with negation, an 87 %, and 85 % f1 score was achieved for binary and multi-classification respectively. Second, the work introduced a manually annotated Arabic_Dep_tweets_10,000 corpus of 10.000 Arabic tweets, which covered neutral tweets as well as a variety of depressed and happy terms. In addition, two automatically annotated English corpora, Eng_without_negation_60.000 corpus of 60,172 English tweets and Eng_with_negation_57.000 corpus of 57,392 English tweets. Both covered a wide range of depressed and cheerful terms; however, Negation was included in the Eng_with_negation_57.000 corpus. Finally, this paper exposes a depression-detection web application which implements our optimal models to detect tweets that contain depression symptoms and predict depression trends for a person either using English or Arabic language.

Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature.

This chapter presents a practical guide for conducting sentiment analysis using Natural Language Processing (NLP) techniques in the domain of tick-borne disease text. The aim is to demonstrate the process of how the presence of bias in the discourse surrounding chronic manifestations of the disease can be evaluated. The goal is to use a dataset of 5643 abstracts collected from scientific journals on the topic of chronic Lyme disease to demonstrate using Python, the steps for conducting sentiment analysis using pretrained language models and the process of validating the preliminary results using both interpretable machine learning tools, as well as a novel methodology of leveraging emerging state-of-the-art large language models like ChatGPT. This serves as a useful resource for researchers and practitioners interested in using NLP techniques for sentiment analysis in the medical domain.

A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data.

BACKGROUND: Health care providers and health-related researchers face significant challenges when applying sentiment analysis tools to health-related free-text survey data. Most state-of-the-art applications were developed in domains such as social media, and their performance in the health care context remains relatively unknown. Moreover, existing studies indicate that these tools often lack accuracy and produce inconsistent results. OBJECTIVE: This study aims to address the lack of comparative analysis on sentiment analysis tools applied to health-related free-text survey data in the context of COVID-19. The objective was to automatically predict sentence sentiment for 2 independent COVID-19 survey data sets from the National Institutes of Health and Stanford University. METHODS: Gold standard labels were created for a subset of each data set using a panel of human raters. We compared 8 state-of-the-art sentiment analysis tools on both data sets to evaluate variability and disagreement across tools. In addition, few-shot learning was explored by fine-tuning Open Pre-Trained Transformers (OPT; a large language model [LLM] with publicly available weights) using a small annotated subset and zero-shot learning using ChatGPT (an LLM without available weights). RESULTS: The comparison of sentiment analysis tools revealed high variability and disagreement across the evaluated tools when applied to health-related survey data. OPT and ChatGPT demonstrated superior performance, outperforming all other sentiment analysis tools. Moreover, ChatGPT outperformed OPT, exhibited higher accuracy by 6% and higher F-measure by 4% to 7%. CONCLUSIONS: This study demonstrates the effectiveness of LLMs, particularly the few-shot learning and zero-shot learning approaches, in the sentiment analysis of health-related survey data. These results have implications for saving human labor and improving efficiency in sentiment analysis tasks, contributing to advancements in the field of automated sentiment analysis.