Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea

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It also examines Internet posts to understand the attitudes and reasons associated with vaccine-related opinions. Methods We collected 500,558 posts over 60 weeks from the Blind platform, mainly used by working individuals, and 854 relevant posts were analyzed. After excluding duplicates and irrelevant content, attitudes toward and reasons for vaccine opinions were studied through sentiment analysis. The study further correlated these categorized attitudes with the actual vaccination data. Results The proportions of posts expressing positive, negative, and neutral attitudes toward COVID-19 vaccines were 5%, 83%, and 12%, respectively. The total post count showed a positive correlation with the vaccination rate, indicating a high correlation between the number of negative posts about the vaccine and the vaccination rate. Negative attitudes were predominantly associated with societal distrust and perceived oppression. Conclusions This study demonstrates the interplay between public perceptions of COVID-19 vaccines as expressed through social media and vaccination behavior. These correlations can serve as useful clues for devising effective vaccination strategies. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-96", "name": "Sentiment analysis of internet posts on vaccination using ChatGPT..." } } ] } Home Browse Sentiment analysis of internet posts on vaccination using ChatGPT... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Park S. Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.12688/f1000research.145845.4 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] Sunyoung Park https://orcid.org/0000-0003-1973-0073 Sunyoung Park https://orcid.org/0000-0003-1973-0073 PUBLISHED 24 Sep 2025 Author details Author details Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, Gyeonggi-do, 10444, South Korea Sunyoung Park Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Sociology of Vaccines collection. This article is included in the Coronavirus (COVID-19) collection. Abstract Background This study used ChatGPT for sentiment analysis to investigate the possible links between online sentiments and COVID-19 vaccination rates. It also examines Internet posts to understand the attitudes and reasons associated with vaccine-related opinions. Methods We collected 500,558 posts over 60 weeks from the Blind platform, mainly used by working individuals, and 854 relevant posts were analyzed. After excluding duplicates and irrelevant content, attitudes toward and reasons for vaccine opinions were studied through sentiment analysis. The study further correlated these categorized attitudes with the actual vaccination data. Results The proportions of posts expressing positive, negative, and neutral attitudes toward COVID-19 vaccines were 5%, 83%, and 12%, respectively. The total post count showed a positive correlation with the vaccination rate, indicating a high correlation between the number of negative posts about the vaccine and the vaccination rate. Negative attitudes were predominantly associated with societal distrust and perceived oppression. Conclusions This study demonstrates the interplay between public perceptions of COVID-19 vaccines as expressed through social media and vaccination behavior. These correlations can serve as useful clues for devising effective vaccination strategies. READ ALL READ LESS Keywords COVID-19, vaccination, ChatGPT, sentiment analysis Corresponding Author(s) Sunyoung Park ( [email protected] ) Close Corresponding author: Sunyoung Park Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Park S. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Park S. Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.12688/f1000research.145845.4 ) First published: 15 Feb 2024, 13 :96 ( https://doi.org/10.12688/f1000research.145845.1 ) Latest published: 24 Sep 2025, 13 :96 ( https://doi.org/10.12688/f1000research.145845.4 ) Revised Amendments from Version 3 In this revised version, several important changes were made in response to reviewer feedback: Title/Abstract: The title and abstract remains unchanged. Literature Review: We expanded the review to include additional studies, such as Argyris et al. (2024), to situate our findings within the broader context of NLP and vaccine discourse research. Methods: We elaborated on the characteristics and limitations of using the Blind platform, including potential workplace-related sentiment bias and issues of representativeness. We also described our preprocessing pipeline more explicitly, including the removal of emojis and its implications. Validation: The validation dataset was expanded from 100 to 200 posts, and details of the coding scheme were clarified. Concordance rates between two board-certified psychiatrists and ChatGPT outputs were reported. Figures/Tables: Figure 2 was redesigned with clearer visualization of association rules and an expanded caption. A confusion matrix was added as supplementary material to strengthen reproducibility. Results/Discussion: We provided more narrative explanation of association rule results, emphasized the paradox between negative sentiment and high vaccination rates, and discussed possible workplace and social coercion effects. We also clarified the methodological limitations and transparency. Data and Code: To enhance reproducibility, we updated the GitHub repository with preprocessing scripts, prompts, and R code, while noting ethical restrictions on sharing SNS posts. These revisions strengthen the methodological transparency, contextual grounding, and interpretability of our findings. In this revised version, several important changes were made in response to reviewer feedback: Title/Abstract: The title and abstract remains unchanged. Literature Review: We expanded the review to include additional studies, such as Argyris et al. (2024), to situate our findings within the broader context of NLP and vaccine discourse research. Methods: We elaborated on the characteristics and limitations of using the Blind platform, including potential workplace-related sentiment bias and issues of representativeness. We also described our preprocessing pipeline more explicitly, including the removal of emojis and its implications. Validation: The validation dataset was expanded from 100 to 200 posts, and details of the coding scheme were clarified. Concordance rates between two board-certified psychiatrists and ChatGPT outputs were reported. Figures/Tables: Figure 2 was redesigned with clearer visualization of association rules and an expanded caption. A confusion matrix was added as supplementary material to strengthen reproducibility. Results/Discussion: We provided more narrative explanation of association rule results, emphasized the paradox between negative sentiment and high vaccination rates, and discussed possible workplace and social coercion effects. We also clarified the methodological limitations and transparency. Data and Code: To enhance reproducibility, we updated the GitHub repository with preprocessing scripts, prompts, and R code, while noting ethical restrictions on sharing SNS posts. These revisions strengthen the methodological transparency, contextual grounding, and interpretability of our findings. See the author's detailed response to the review by Cihan Cilgin See the author's detailed response to the review by Ummu Fatihah Mohd Bahrin See the author's detailed response to the review by Young Anna Argyris READ REVIEWER RESPONSES Introduction The exchange of opinions and information on social media platforms has become vital for social interaction and communication. This transformation has created an environment in which information and opinions about societal issues spread rapidly and are shared. 1 In recent years, the global landscape has been significantly affected by the COVID-19 pandemic, leading to an abundance of Internet posts discussing various aspects of this crisis. Among these critical topics, vaccination has emerged as an indispensable tool for overcoming the challenges posed by the pandemic. 2 Despite their importance, the distribution and vaccination rates vary significantly across nations, regions, and occupations, 3 and these differences are closely related to individuals’ perceptions of and attitudes toward vaccines. 4 , 5 Gathering public opinion on vaccination through Internet posts is anticipated to contribute to providing accurate information and obtaining insights for policy formulation in infectious disease prevention. 6 Sentiment analysis is commonly employed to analyze extensive and diverse online content. It is a form of text classification that focuses on subjective statements and is often called opinion mining. The goal was to analyze opinions to gain insights into public perceptions. 7 However, the complexity and necessity of meticulous processes to enhance accuracy pose challenges for sentiment analysis. 8 Moreover, the language used on social media platforms can vary in style among users and may require understanding within the context. In such cases, existing sentiment-analysis models may find it challenging to adapt to diversity and changes. 9 Therefore, in this study, we employed ChatGPT, a state-of-the-art language model recognized for its natural language understanding capabilities, to explore the nuanced insights derived from the analysis. 10 ChatGPT excels in contextual understanding, allowing comprehension of the nuanced meanings within the context of a conversation. This contextual awareness is particularly valuable in sentiment analysis, where the interpretation of sentiments often relies on an understanding of the surrounding text. In addition, ChatGPT is adaptable to diverse language nuances, capturing the intricacies of expression across different linguistic styles, cultural variations, and demographic factors. 11 This adaptability can be crucial in sentiment analysis, especially when dealing with social media discourse, where language can be highly dynamic and varied. In particular, the sentiment analysis conducted in this study using ChatGPT holds several strengths. Firstly, the objective was to compare the attitudes towards vaccines identified in online content with actual vaccination rates. While analyzing data extracted from internet posts through sentiment analysis contributes to understanding a multitude of opinions, there has been a limitation in verifying how these expressed opinions relate to real-world behavioral outcomes. Analyzing the relationship with societal behavioral outcomes, as reflected in actual vaccination rates, will aid in comprehending the results of sentiment analysis and considering future analytical directions. 12 Secondly, sentiment analysis conducted in Korean, a relatively smaller cultural sphere, has encountered limitations despite various approaches. This study sought to explore the extent to which ChatGPT could contribute to understanding the subtle nuances in internet posts written by diverse individuals in the Korean language. 13 This study aimed to conduct a sentiment analysis of Internet postings using OpenAI GPT-3.5-turbo model, exploring the potential associations between diverse opinions expressed on social media and actual vaccination rates. The goal was to investigate the causal relationship between sentiments observed through sentiment analysis and real-world behavior. We hypothesized that a higher prevalence of positive vaccine-related posts correlates with elevated vaccination rates. Additionally, the research intended to further analyze the reasons associated with attitudes toward vaccines as expressed in internet posts. Literature review Recent studies have provided significant insights by analyzing public sentiment towards COVID-19 vaccines on social media. These studies primarily identify positive or negative reactions to evaluate public perceptions of the vaccine. 14 Sentiment analysis of tweets using natural language processing techniques has been effective in analyzing changing attitudes toward vaccines over time and across different countries. Analyzing 11 million tweets from 180 countries revealed an increasing trend in positive responses to vaccines over time. 15 Furthermore, it was found that providing sufficient information is essential for fostering positive attitudes toward vaccines. 16 In a similar vein, Nelson et al. (2024) 17 employed NLP to analyze online vaccine discourse and discovered statistically significant correlations between social media sentiments and vaccine uptake. Their findings indicated that regions with a higher proportion of positive vaccine-related posts experienced higher vaccination rates, thereby reinforcing the potential of using social media data as an early indicator for public health trends. Such results underscore the importance of context-specific analyses. This data is crucial for formulating public health policies and communication strategies related to vaccines. When examining the correlation between these results and actual vaccination rates, it was reported that higher numbers of positive tweets correlate with higher vaccination rates. 15 Additionally, studies have shown that regions with more positive tweets tend to have higher vaccination rates, especially among populations aged 40 and above. 18 However, distrust and misinformation also contribute to lower vaccination rates. 19 Therefore, providing accurate information and fostering positive public sentiment are essential to increase vaccination rates. To achieve reliable results in these studies, effective sentiment analysis is essential. However, sentiment analysis of tweets presents several challenges, such as sarcasm, irony, and language-specific challenges. 20 Current natural language processing technologies like Vader and TextBlob are mostly optimized for English data, making them difficult to use for analyzing minor languages like Korean. 21 Even when new Korean lexicons are developed for research, their accuracy ranges from 40-70%, which leads to inconsistent results. 22 Therefore, advanced AI-based natural language processing tools like ChatGPT, which excel in understanding complex language patterns and nuances, show promise in achieving high accuracy across various languages and dialects. 23 This capability is expected to enhance the reliability of sentiment analysis. Methods This observational study analyzed users’ perceptions of the COVID-19 vaccine on Internet platforms targeting working individuals and their relationship with actual vaccination rates. This study, involving the collection and analysis of publicly available internet posts without containing personal information, received approval for a consent waiver and exemption from review through the National Health Insurance Service Ilsan Hospital Institutional Review Board (IRB) (NHIMC-2023-08-028). Web crawling and data collection On the Internet, there are cases in which individuals intentionally post multiple messages to emphasize their claims or engage in actions with specific intentions, such as marketing. 24 To counteract this form of online manipulation, we utilized web crawling on the social network service (SNS) ‘Blind ( https://www.teamblind.com/kr/ ),’ where individuals involved in employment, job-seeking, and workplace organizations actively participate and interact. Users join this SNS by using their individual email accounts associated with their respective workplaces, anonymously. Blind is organized into company- and industry-specific forums where workplace-related topics, such as salary negotiations, management pressure, and compliance with company policies, are frequently discussed. This structure tends to amplify sentiments specific to professional environments, including concerns about employer-imposed vaccination mandates or job-related repercussions of vaccine refusal. The posts were web-scraped using the Python Selenium package, adhering to the Blind’s Access Restriction Protocol (robots.txt). The data collection period spanned between March 23, 2022, and May 16, 2023, totaling 60 weeks, and a total of 500,558 posts were gathered. Information such as post number, posting date, publicly available workplace information of the post author, post title, and content was collected through web scraping. To ensure the integrity of the dataset, we implemented a robust method for excluding duplicate posts. Each post was uniquely identified based on a combination of attributes, including post number, posting date, and author details. Posts sharing identical attributes were flagged as duplicates, and only the earliest occurrence was retained for analysis. This process aimed to eliminate redundancy and maintain the diversity of opinions within the dataset. After then, posts were filtered on whether they contained the following keywords. Keywords related to COVID-19 were checked, such as COVID-19, COVID, and coronavirus, as well as keywords related to vaccination, for example, vaccination, vaccine, and inoculation. Python’s Beautiful Soup library and Request module were used for this purpose. Keyword searches were conducted on the title and content of each post. Data refinement and pre-processing After excluding duplicate posts and those less relevant to COVID-19, 4,419 posts were curated, with 854 posts specifically mentioning the vaccines chosen for the analysis. The posts underwent text preprocessing, involving UTF-8 encoding, stop word and URL removal, as well as the removal of emojis and special characters. Sentiment analysis and reasoning extraction using ChatGPT Subsequently, the posts were analyzed using the OpenAI GPT-3.5-turbo model. This model, pretrained on a large corpus of language data by OpenAI, classified the attitude expressed in each post toward the vaccine as positive, negative, or neutral, following the criteria commonly used in the sentiment analysis of conditional statements. 7 In cases where the model could not confidently assign an attitude, we instructed it to respond with ‘unclear’. Using ChatGPT, we extracted the reasons for positive or negative attitudes toward vaccines from Internet posts. The system’s role was an AI assistant tasked with identifying the reasons behind the positive or negative views on COVID-19 vaccines in the given posts. The goal was to extract up to three reasons per post. Cases in which the reasons were unknown or difficult to classify were noted accordingly. For validation of sentiment classification, two board-certified psychiatrists independently annotated a set of 200 randomly selected posts, each reviewing 100 unique samples. Posts were labeled as positive, negative, or neutral based on their overall emotional tone and context regarding COVID-19 vaccination. Positive sentiment was identified when posts expressed trust in the vaccine, voluntary willingness, or perceived benefits. Negative sentiment included expressions of fear, mistrust, regret, or coercion, often reflecting pressure from employers or society. Posts that conveyed information in a factual or emotionally neutral tone, without strong evaluative language, were categorized as neutral. Statistical analysis COVID-19 vaccination information was collected from the Korea Disease Control and Prevention Agency daily vaccination status ( https://ncv.kdca.go.kr/vaccineStatus.es?mid=a11710000000 ). The counts for the first and second doses were collected over the 60-week research period, including additional winter booster vaccination counts for 31 weeks starting on October 11, 2022. Considering the working patterns of employees and the lower frequency of vaccination on weekends, a correlation analysis was performed on the sum of the posts and weekly vaccination counts. Furthermore, an association analysis was conducted to explore relationships among the reasons mentioned in the posts, specifically focusing on understanding the relationships between key reasons behind positive and negative attitudes. All analyses were performed using R version 4.2.2. Results The analysis used OpenAI’s gpt-3.5-turbo model, which yielded 851 emotional evaluations. In three cases, the model reported uncertainty; upon manual review, these instances were deemed uncertain and excluded from the analysis. Two psychiatrists evaluated 200 posts, resulting in 84% agreement with the results provided by the gpt-3.5-turbo model. The results of the confusion matrix are presented in the Extended data. Over the 60-week analysis period, the sentiment distribution was as follows: 44 positive (5%), 704 negative (83%), and 103 neutral (12%) posts. For posts collected over 31 weeks starting from October 11, 2022, and associated with additional vaccinations during the winter season, the sentiment distribution was 20 positive (6%), 254 negative (80%), and 43 neutral (14%) posts. The total vaccination counts, weekly vaccination averages, and weekly post-vaccination averages for each study period are presented in Table 1 . Table 1. The mean of posts by sentiment and vaccination types. A. Whole study period (for 60 weeks) Posts Vaccination Positive (N=44) Negative (N=704) Neutral (N=103) Total (N=851) 1 st Dose (N=179053) 2 nd Dose (N=224735) Total (N=403788) Mean ± SD (per week) 0.73 ± 1.01 11.73 ± 8.31 1.72 ± 1.74 14.18 ± 9.67 2984.22 ± 4520.73 3745.58 ± 4038.60 6729.80 ± 8053.79 B. Winter booster vaccination period (for 31 weeks) Postings Winter booster vaccination (N=6682925) Positive (N=20) Negative (N=254) Neutral (N=43) Total (N=317) Mean ± SD (per week) 0.65 ± 0.97 8.19 ± 6.28 1.39 ± 1.45 10.23 ± 7.32 228841.41 ± 205373.47 Relationship between attitudes toward COVID-19 vaccination in internet postings and actual vaccination rates Positive posts regarding vaccination attitudes showed a weak positive correlation with the first-dose vaccination counts. Negative posts on vaccines exhibited a moderately positive correlation with both first- and second-dose vaccinations. Posts expressing a neutral attitude showed a weak positive correlation with second-dose vaccinations ( Table 2 , Figure 1 ). Table 2. Correlation between number of vaccinations and postings by attitude toward vaccines. 1 st Dose 2 nd Dose Total Booster vaccination Positive attitude 0.27 * 0.22 0.26 * 0.50 ** Negative attitude 0.59 ** 0.66 ** 0.66 ** 0.69 ** Neutral attitude 0.24 0.31 * 0.29 * 0.18 Total number of postings 0.58 ** 0.65 ** 0.65 ** 0.69 ** ** The correlation is significant at the 0.01 level (2-tailed). * The correlation is significant at the 0.05 level (2-tailed). Figure 1. Comparison between the number of posts about vaccination (left axis) and actual vaccination (right axis) for 60 weeks. Regarding the booster vaccination count during the winter season, a strong correlation was observed between positive and negative posts regarding vaccination attitudes. The total counts of positive, negative, and neutral posts showed strong correlations with the counts of the first, second, and booster vaccinations. Table 2 presents the correlation results. Extraction of attitudinal reasons toward vaccination and association analysis between reasons Using ChatGPT, we extracted the reasons for positive or negative attitudes toward vaccines from Internet posts. Among the 44 positive posts, the most prevalent (73%) was prevention, cited in 39 instances. For the 704 negative posts, the most common reason (28%) was distrust of the social system, which was mentioned in 194 cases. Two psychiatrists manually reviewed 200 posts and compared the outcomes in the extracted results. For the first reason per post, there were 58 instances of agreement, 37 for the second reason, and 14 for the third reason. We conducted association rule analysis on the reasons extracted from online posts regarding COVID-19 vaccination. Using ChatGPT, we prompted the model to act as an AI assistant tasked with identifying up to three reasons per post explaining the user's positive or negative attitude toward the vaccine. Posts in which reasons were unclear or could not be extracted were excluded from further analysis. The extracted free-text reasons were subsequently categorized into predefined item labels by two psychiatrists, based on thematic similarity. This categorization aimed to enable structured analysis and was guided by a codebook comprising eight positive and six negative categories. Positive categories included prevention, symptom alleviation, decreased mortality, effectiveness, safety, fewer side effects, containment of infection spread, and immune system reinforcement . Negative categories included mistrust of the social system, antipathy toward social oppression, side effects, concerns about side effects, lack of information, and perception of insufficient efficacy . For example, expressions such as “to protect others” or “not to spread the virus” were mapped to prevention , while phrases like “I don’t trust the authorities” were classified as mistrust . Due to low interrater agreement (14%) on the third reason during manual review, only the first and second categorized reasons were used for association analysis. Ultimately, 36 distinct groups were analyzed for positive sentiment and 381 for negative sentiment. In the analysis of positive reasons, “Decreasing mortality rate” is associated with “Symptom alleviation,” showcasing a support of 5.71%, confidence of 50.00%, and a lift of 0.11. Additionally, instances of “Post-recovery symptom alleviation” (support = 5.71%, confidence = 100.00%, lift = 0.06), “Immune activity strengthening” (support = 17.14%, confidence = 100.00%, lift = 0.17), “Inhibition of infection spread” (support = 31.43%, confidence = 100.00%, lift = 0.31), and “Safety” (support = 14.29%, confidence = 83.33%, lift = 0.17) are correlated with the item of “Prevention.” In the analysis of negative reasons, ‘Antipathy to social oppression’ strongly correlates with ‘Mistrust’ (support = 21.78%, confidence = 94.32%, lift = 2.16). Conversely, ‘Mistrust’ is moderately associated with ‘Antipathy to social oppression’ (support = 21.78%, confidence = 50.00%, lift = 2.16). Instances involving concerns about underlying conditions were linked to side effects (support = 29.13%, confidence = 91.74%, lift = 1.26). Conversely, side effects were associated with concerns about the underlying conditions (support = 29.13%, confidence = 40.07%, lift = 1.26). The presence of “lack of information” was strongly associated with”unknown side effects” (support = 23.62%, confidence = 90.91%, lift = 1.25). The overall trends in the association analyses are shown in Figure 2 . Figure 2. Association rules visualization for vaccine sentiments: positive (A) and negative (B) reasons. Each node represents a reason extracted from social media posts. Arrows indicate the direction of association from antecedent (left) to consequent (right). The thickness of each arrow reflects the strength of association (lift), while the arrow color corresponds to the confidence value. Lighter arrow colors reflect higher confidence values. Discussion This study explored the relationship between professionals’ attitudes toward the COVID-19 vaccine as expressed in internet posts and actual vaccination rates. Additionally, it examined the positive and negative reasons associated with vaccine attitudes and investigated the relationships between them. The sentiment analysis conducted using OpenAI’s gpt-3.5-turbo model demonstrated an 84% concordance rate when compared with evaluations by mental health specialists. This result indicates a substantial level of accuracy of the model and affirms the utility of automated analysis using natural language processing technology. Recent studies using deep-learning-based analysis models have reported sentiment analysis accuracies ranging from approximately 70% to 90%. These studies often evaluate sentiments in posts related to COVID-19 on major social media platforms, such as Twitter. 25 , 26 In particular, the accuracy for positive posts was significantly decreased in this study. Data imbalances in sentiment analysis could be a cause of this, leading to models overfitting. This results in proficient detection of negative sentiments—due to their prevalence in training datasets—and reduced accuracy in identifying positive sentiments. 27 The reasoning analysis in this study, which aimed at extracting the reasons behind positive or negative sentiments toward vaccines, showed a lower matching rate (58%). Compared to sentiment analysis, which gauges the overall sentiment, reasoning analysis delves into specific words or phrases that explain why a sentiment is expressed. This process involves a deeper understanding of language nuances and contextual cues, making it a more intricate task. 28 Furthermore, ChatGPT tends to include irrelevant content when producing results for short posts, making it challenging to infer the reasons. This behavior is likely attributable to the generative nature of ChatGPT, a characteristic inherent in creative AI models. 11 Previous survey studies have shown that negative opinions towards vaccines accounted for 39.8%, 29 whereas in the present study, negative opinions are markedly higher at 82%. These results are associated with certain social phenomena in which negative or extreme online content is prevalent. Cyber venting 30 , 31 is a phenomenon in which internet users express dissatisfaction, stress, or anger online, making it easy to express various negative emotions related to vaccination, discomfort about side effects, and social pressure. Moreover, the freedom to swiftly respond and exchange opinions through comments in anonymous online spaces can result in uninhibited emotional expressions. 32 , 33 This phenomenon was evident in the posts analyzed in this study, in which various expressions of anxiety and discomfort regarding vaccine side effects, derogatory remarks about compliant attitudes toward vaccination, and rumors related to vaccines were observed. In this study, the correlation analysis between the number of posts and vaccine doses administered indicated a strong correlation between the total number of positive, negative, and neutral posts and the counts of the first, second, and additional vaccine doses. Numerous studies have explored the relevance of social media platforms to epidemiological patterns and medical information. 34 For instance, even before the COVID-19 pandemic, research suggested a strong correlation between the regional distribution of social media posts related to infectious diseases and their actual spread. 35 Additionally, studies on trend analysis using search queries have shown associations with the spread of infectious diseases. 36 In this study, the diverse opinions expressed online before and after the vaccination could be considered a reflection of the various perspectives emerging on online platforms. Interestingly, the strong correlation between negative sentiment and actual vaccination rates highlights a paradox: although online discourse revealed widespread concerns, mistrust, and discomfort regarding vaccination, these attitudes did not prevent high uptake. The strong correlation between these negative opinions and actual vaccination rates can be inferred from the content of internet posts. In Korea, the total vaccination rate reported by the Korea Disease Control and Prevention Agency was very high at 96.9%. However, many online posts indicated that individuals chose to be vaccinated not out of personal conviction or trust in public health authorities, but due to external pressures—such as workplace mandates, fear of social exclusion, or concerns about job security. Some posts even described feelings of coercion or moral fatigue caused by persistent social expectations. This suggests that the high vaccination rate may not accurately reflect genuine public support or confidence in vaccination, but rather compliance driven by institutional or social obligation. This finding underscores the complex and often contradictory relationship between public health behavior and the sentiments expressed in online discourse. It also highlights the importance of interpreting social media signals in context, especially in cultures where collective norms and workplace hierarchies strongly influence individual decision-making. In this study, implications were derived by analyzing the underlying factors influencing attitudes toward vaccines. For positive attitudes toward vaccines, individuals expected preventive effects, personal relief from symptoms and after-effects, and societal benefits, such as preventing the spread of infection. In contrast, negative attitudes toward vaccines were associated with resentment and distrust toward social oppression. This aligns with discussions in numerous studies during the pandemic, indicating that public distrust of efforts to prevent epidemics at the societal level can lead to strong resistance. 37 – 39 Information gaps in various media channels regarding vaccines can reinforce public anxiety and contribute to negative attitudes. 40 Therefore, it is crucial to implement effective communication strategies and educational initiatives to address these concerns and promote a more informed perspective on vaccination. 41 , 42 The limitations of this study include its focus on SNS users among employed individuals, warranting future research that encompasses diverse demographic groups and regions for a more comprehensive understanding. Furthermore, research incorporating various factors is essential to investigate the relationship between opinion formation on Internet platforms and vaccination behavior. Additionally, limited number of posts poses significant constraints on this study, compared to other studies. 6 , 35 This limitation may be attributed to the fact that the BLIND community, which was the focus of the analysis, primarily targets professionals, resulting in a predominance of posts related to salary, career, and other job-related topics. Blind’s user base primarily consists of white-collar professionals in corporate settings, which may introduce a specific bias related to organizational culture, hierarchy, and employment-related consequences of vaccination decisions. As the platform fosters discussions centered on employment and company policies, sentiments expressed may be shaped more by perceived social or workplace obligations than by personal health beliefs. The period of the study, which occurred about one year after the initial vaccine rollout in February 2022, coincided with a significant decrease in public interest in vaccines, which also likely contributed to the fewer posts related to vaccines. Moreover, the analysis of sentiment analysis models, including the gpt-3.5-turbo model, reveals additional limitations. These models may not fully capture the subtle nature of emotions, and the interpretation of emotional expressions can vary among individuals. 11 Therefore, involving a diverse range of reviewers across different age groups and backgrounds for manual review is crucial. Considering the gpt-3.5-turbo model, there is a limitation in fully understanding subtle nuances and cultural contexts. This is particularly evident in contexts like Korea, characterized by unique language styles, cultural norms, and demographic characteristics. 13 Another limitation of this study is the removal of emojis during text preprocessing. Although emojis convey emotional nuance, they were excluded to ensure consistency and avoid ambiguity, as ChatGPT is optimized for natural language rather than symbolic elements like emojis, particularly in Korean. However, this may have led to the loss of subtle affective cues, which future studies could address using emoji-aware models. Building on these limitations, this study also encountered challenges in addressing data imbalance during sentiment analysis. Traditional undersampling methods were unsuitable due to the limited dataset size, as reducing the number of negative postings could compromise the reliability of correlation analysis. Similarly, oversampling techniques such as SMOTE, which generate synthetic data in vector form, were impractical for sentiment analysis using language models like ChatGPT. To address these issues, data augmentation was performed using ChatGPT 43 with controlled prompts to diversify positive and neutral data while maintaining contextual consistency. Despite these efforts, the sentiment analysis results did not improve after augmentation. Positive and neutral data proportions were increased to 25-30% of the total dataset, yet the accuracy declined, with neutral data frequently misclassified as negative. This decline likely stemmed from the nondeterministic nature of ChatGPT, 44 leading to inconsistencies in the augmented data, and potential quality degradation caused by inaccuracies in the initial analysis, particularly in ambiguous or sarcastic cases. These findings further underscore the complexities and limitations of applying language models like ChatGPT to sentiment analysis, particularly in culturally nuanced contexts. In conclusion, this study revealed the interaction between the public’s perception of the COVID-19 vaccine expressed on social media and their actual vaccination behavior. The perception of risk and willingness to be vaccinated can be influenced by various mass media sources, such as the news. Opinions encountered on SNS, which people use, are also likely to significantly impact individuals’ perceptions of vaccines due to biased approaches to information and the phenomenon of conformity. Therefore, implementing social strategies that provide appropriate vaccine information in an accessible manner is crucial. Ethical considerations This study was exempted from review by the National Health Insurance Ilsan Hospital Institutional Review Board (NHIMC-2023-08-028) 04/09/2023. Data availability Vaccination data Zenodo: Korea Disease Control and Prevention Agency daily COVID19 vaccination status, https://zenodo.org/doi/10.5281/zenodo.10252895 . 45 Vaccination data is accessible in the form of an Excel file. This file comprehensively includes vaccination rate information relevant to the research findings. COVID-19 vaccination information was collected from the Korea Disease Control and Prevention Agency daily vaccination status ( https://ncv.kdca.go.kr/vaccineStatus.es?mid=a11710000000 ). For additional details or specific requests regarding data provision, feel free to contact us. SNS crawling data The data used for SNS crawling in this study, acquired through social media crawling, cannot be shared due to ethical and copyright restrictions related to social media content. A comprehensive description of the methodology is presented in the Methods section, along with the Python code below, facilitating the replication of the study. For inquiries concerning the methodology, please direct any questions to the corresponding author. Python code for data scraping and sentiment analysis https://github.com/bechungan/Scraping-and-Sentiment-Analysis-using-CGPT . Reporting guidelines Zenodo: STROBE checklist for Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea, https://doi.org/10.5281/zenodo.10429910 . Extended data Zenodo: The result of the confusion matrix for the accuracy evaluation of 200 posts. This project contains the data: confusion matrix.docx. https://doi.org/10.5281/zenodo.16732573 . 46 Acknowledgments We extend our sincere appreciation to G-J Park at Cheongdam SL Clinic for invaluable guidance and assistance in data scraping and analysis. Additionally, we express our gratitude to Dr. J. Ahn, a psychiatrist, at Paju Psychiatric Clinic for providing valuable assistance in reviewing the data. 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Publisher Full Text Comments on this article Comments (0) Version 4 VERSION 4 PUBLISHED 15 Feb 2024 ADD YOUR COMMENT Comment Author details Author details Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, Gyeonggi-do, 10444, South Korea Sunyoung Park Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (4) version 4 Revised Published: 24 Sep 2025, 13:96 https://doi.org/10.12688/f1000research.145845.4 version 3 Revised Published: 17 Jan 2025, 13:96 https://doi.org/10.12688/f1000research.145845.3 version 2 Revised Published: 02 Sep 2024, 13:96 https://doi.org/10.12688/f1000research.145845.2 version 1 Published: 15 Feb 2024, 13:96 https://doi.org/10.12688/f1000research.145845.1 Copyright © 2025 Park S. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Park S. Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.12688/f1000research.145845.4 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 4 VERSION 4 PUBLISHED 24 Sep 2025 Revised Views 0 Cite How to cite this report: Mohd Bahrin UF. Reviewer Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.186240.r417210 ) The direct URL for this report is: https://f1000research.com/articles/13-96/v4#referee-response-417210 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 14 Oct 2025 Ummu Fatihah Mohd Bahrin , Universiti Teknologi MARA Cawangan Terengganu, Kuala Terengganu, Malaysia Approved VIEWS 0 https://doi.org/10.5256/f1000research.186240.r417210 I have carefully gone through the revisions made, and I am satisfied with the ... Continue reading READ ALL I have carefully gone through the revisions made, and I am satisfied with the improvements. The authors have sufficiently addressed my previous comments, and I have no further reservations. Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Mohd Bahrin UF. Reviewer Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.186240.r417210 ) The direct URL for this report is: https://f1000research.com/articles/13-96/v4#referee-response-417210 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 3 VERSION 3 PUBLISHED 17 Jan 2025 Revised Views 0 Cite How to cite this report: Argyris YA. Reviewer Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.176895.r382353 ) The direct URL for this report is: https://f1000research.com/articles/13-96/v3#referee-response-382353 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 19 Jun 2025 Young Anna Argyris , Michigan State University, East Lansing, Michigan, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.176895.r382353 Overview This paper addresses a crucial issue—public sentiment toward COVID-19 vaccination in South Korea—and examines it through a novel approach: utilizing ChatGPT-based sentiment analysis on social media data. The study's attempt to connect sentiment trends to real-world vaccination rates ... Continue reading READ ALL Overview This paper addresses a crucial issue—public sentiment toward COVID-19 vaccination in South Korea—and examines it through a novel approach: utilizing ChatGPT-based sentiment analysis on social media data. The study's attempt to connect sentiment trends to real-world vaccination rates is timely and commendable, and the use of advanced language models is a noteworthy methodological innovation. However, the manuscript has several substantive issues that prevent it from being indexed in its current form. While I highly appreciate the authors’ effort and believe this line of inquiry is valuable, the concerns outlined below—including methodological limitations and unclear contributions —need to be addressed more fully. I share these critiques in the hope that they will help the authors improve their paper. Literature Review The authors may also consider referencing a similar approach in Argyris et al.'s study published in Social Science & Medicine, which also used natural language processing to analyze online vaccine discourse: (Ref.1). They also demonstrate significant correlations between social media posts and vaccination rates, and elaborate potential reasons for the associations, which might be helpful for the authors to contextualize the contribution of this paper within the broader literature. Methods Note: Other reviewers have already addressed other issues, such as data imbalance and small sample size, and are not reiterated here. The authors specify that posts were collected from Blind, an anonymous social media community targeted at working professionals. While this is helpful, the implications of using a niche platform with a specific user demographic should be discussed more thoroughly, particularly regarding the representativeness of the data, ethical considerations related to consent and privacy, and the generalizability of the study’s findings. To their credit, the authors do acknowledge this limitation in the discussion section, noting that the Blind platform primarily targets professionals and may not capture broader public sentiment. However, merely acknowledging this limitation does not fully address the methodological implications. The restricted demographic may systematically bias the types of sentiments expressed, especially given that workplace pressures and professional norms likely influence how vaccination is discussed on the platform. Blind is organized into numerous company- and industry-specific groups, which may affect the topics and tone of discussions, as well as amplify particular workplace-related sentiments such as coercion or pressure to comply with vaccination mandates. The posts underwent text preprocessing, which involved UTF-8 encoding, removal of stop words and URLs, as well as removal of emojis and special characters. While such cleaning is common, the removal of emojis may be questionable, as emojis—though not crucial—can be useful for sentiment analysis and emotional nuance detection. The authors should justify this decision or acknowledge its potential implications for the analysis. A major concern is the reliability of ChatGPT in accurately classifying sentiment in the analyzed posts. The authors report an 85% agreement rate between the model and two psychiatrists, but this evaluation was conducted on only 100 posts out of 854 analyzed. Given the complexity and nuance of language in social media discourse—particularly when dealing with sarcasm, irony, or emotionally charged content—a validation sample of 100 posts is insufficient to establish confidence in the model's overall performance. A more rigorous evaluation, ideally with interrater comparisons across a larger and more diverse subset of posts, is necessary to support the claim that the model accurately reflects user sentiment. The authors should clarify the coding scheme. Results and Visualization Figure 2 is very difficult to understand. Based on the accompanying description, the authors appear to be using association rule mining to identify co-occurring sentiment categories within the reasons given for or against vaccination. For example, they report support, confidence, and lift values to indicate the strength and reliability of relationships like “Decreasing mortality rate” being associated with “Symptom alleviation,” or “Antipathy to social oppression” with “Mistrust.” However, the figure itself lacks clear labels, an intuitive layout, and an adequate explanation in the caption. The connection between the visual elements and these statistical measures is not readily apparent. The figure would benefit from a simplified design, a clearer legend, and a more detailed narrative in the main text to help readers accurately interpret the findings. For guidance on more effective visualizations of association rule mining results, the authors may refer to Dolores et al. (2023) - (Ref 2). Table 4, which presents a clear and practical summary of association rule metrics and their interpretations. Discussion Interestingly, the study finds a strong correlation between the number of negative posts and actual vaccination rates, despite the overwhelmingly negative sentiment expressed in the data. The authors acknowledge that many individuals were vaccinated not out of conviction, but because of job requirements or social pressure. In this context, the findings suggest that high vaccination rates in Korea (reported at 96.9%) may reflect compliance driven by external pressures rather than positive public sentiment. This nuance is important: public health behavior may not align with expressed beliefs, particularly in environments where vaccination is perceived as socially or professionally obligatory. The authors could emphasize this tension more clearly to avoid the mistaken interpretation that high uptake equates to high trust or positive attitudes toward vaccines. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? No References 1. Nelson V, Bashyal B, Tan P, Argyris Y: Vaccine rhetoric on social media and COVID-19 vaccine uptake rates: A triangulation using self-reported vaccine acceptance. Social Science & Medicine . 2024; 348 . Publisher Full Text 2. Dolores M, Fernandez-Basso C, Gómez-Romero J, Martin-Bautista M: A big data association rule mining based approach for energy building behaviour analysis in an IoT environment. Scientific Reports . 2023; 13 (1). Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Information Systems, Health Communication, Computational method I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Argyris YA. Reviewer Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.176895.r382353 ) The direct URL for this report is: https://f1000research.com/articles/13-96/v3#referee-response-382353 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 05 Sep 2025 Sunyoung Park , Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, 10444, South Korea 05 Sep 2025 Author Response 1. Literature Review: Reference to Argyris et al. : Thank you for the suggestion. We agree that the study by Argyris et al. provides important contextual support for our work. We ... Continue reading 1. Literature Review: Reference to Argyris et al. : Thank you for the suggestion. We agree that the study by Argyris et al. provides important contextual support for our work. We have revised the Literature Review section to include and discuss this reference. Specifically, we highlight how our study contributes to the growing body of research connecting social media sentiment with real-world vaccine behaviors, with a distinct focus on Korean-language data and the use of ChatGPT. 2. Methods – Use of Blind and Demographic Limitations: This is an important point. While we acknowledged this limitation, we agree that the methodological implications should be expanded. We have added a detailed discussion in both the Methods and Discussion sections, emphasizing how Blind’s focus on professionals may introduce bias, particularly regarding expressions of workplace-related pressure to vaccinate. We also discuss how company- and industry-specific groupings may affect discourse tone and topic. 3. Removal of Emojis in Preprocessing: We thank the reviewer for this valuable observation. We have now acknowledged in the Methods section that while emojis can contribute emotional context to sentiment analysis, they were removed to reduce preprocessing complexity and ensure consistency in textual inputs for the language model. We have also included a note in the Limitations section about the possible loss of emotional signals due to this decision. 4. Validation Sample Size and Coding Scheme: Thank you for this insightful comment. In response, we have expanded the validation sample from 100 to 200 posts to improve the reliability of our sentiment classification. The additional validation yielded a concordance rate of 84% between the two board-certified psychiatrists, which is consistent with the initial agreement rate. We have also provided a more detailed description of our coding scheme in the Methods section. Posts were categorized as positive, negative, or neutral based on the emotional tone and content related to COVID-19 vaccination. Annotation was guided by clinical criteria commonly used in psychiatric evaluation of affective expression, including linguistic cues (e.g., appraisal, sarcasm, urgency), expressed intentions (e.g., compliance, avoidance), and context (e.g., workplace influence, medical risk). Disagreements between annotators were resolved through discussion and consensus. These revisions are now reflected in the updated manuscript. 5. Figure 2 – Difficult Interpretation of Association Rules: Thank you for pointing this out. We have redesigned Figure 2 using a more intuitive layout and clearer legends. The caption has been expanded to explain what each element represents (support, confidence, lift), and we’ve added a narrative in the Results section that walks through key associations. 6. Discussion – Clarify the Tension Between Negative Sentiment and High Vaccination Rates: We fully agree. In the revised Discussion, we now highlight this discrepancy more explicitly. We discuss how high vaccine uptake in Korea may have been driven by workplace mandates or social expectations rather than positive sentiment, reinforcing the complex dynamics between public behavior and online discourse. 7. Reproducibility and Methodological Transparency: To enhance reproducibility, we have updated our GitHub repository to include the full set of preprocessing scripts, sentiment analysis prompts used with ChatGPT, and R code used for association rule mining. We have described the process of categorizing variables for reasoning analysis in more detail in the main text. We have also clarified in the manuscript that the SNS post data cannot be shared due to ethical and copyright concerns. However, we ensured that sufficient methodological details are provided to allow replication of our process using alternative datasets. 1. Literature Review: Reference to Argyris et al. : Thank you for the suggestion. We agree that the study by Argyris et al. provides important contextual support for our work. We have revised the Literature Review section to include and discuss this reference. Specifically, we highlight how our study contributes to the growing body of research connecting social media sentiment with real-world vaccine behaviors, with a distinct focus on Korean-language data and the use of ChatGPT. 2. Methods – Use of Blind and Demographic Limitations: This is an important point. While we acknowledged this limitation, we agree that the methodological implications should be expanded. We have added a detailed discussion in both the Methods and Discussion sections, emphasizing how Blind’s focus on professionals may introduce bias, particularly regarding expressions of workplace-related pressure to vaccinate. We also discuss how company- and industry-specific groupings may affect discourse tone and topic. 3. Removal of Emojis in Preprocessing: We thank the reviewer for this valuable observation. We have now acknowledged in the Methods section that while emojis can contribute emotional context to sentiment analysis, they were removed to reduce preprocessing complexity and ensure consistency in textual inputs for the language model. We have also included a note in the Limitations section about the possible loss of emotional signals due to this decision. 4. Validation Sample Size and Coding Scheme: Thank you for this insightful comment. In response, we have expanded the validation sample from 100 to 200 posts to improve the reliability of our sentiment classification. The additional validation yielded a concordance rate of 84% between the two board-certified psychiatrists, which is consistent with the initial agreement rate. We have also provided a more detailed description of our coding scheme in the Methods section. Posts were categorized as positive, negative, or neutral based on the emotional tone and content related to COVID-19 vaccination. Annotation was guided by clinical criteria commonly used in psychiatric evaluation of affective expression, including linguistic cues (e.g., appraisal, sarcasm, urgency), expressed intentions (e.g., compliance, avoidance), and context (e.g., workplace influence, medical risk). Disagreements between annotators were resolved through discussion and consensus. These revisions are now reflected in the updated manuscript. 5. Figure 2 – Difficult Interpretation of Association Rules: Thank you for pointing this out. We have redesigned Figure 2 using a more intuitive layout and clearer legends. The caption has been expanded to explain what each element represents (support, confidence, lift), and we’ve added a narrative in the Results section that walks through key associations. 6. Discussion – Clarify the Tension Between Negative Sentiment and High Vaccination Rates: We fully agree. In the revised Discussion, we now highlight this discrepancy more explicitly. We discuss how high vaccine uptake in Korea may have been driven by workplace mandates or social expectations rather than positive sentiment, reinforcing the complex dynamics between public behavior and online discourse. 7. Reproducibility and Methodological Transparency: To enhance reproducibility, we have updated our GitHub repository to include the full set of preprocessing scripts, sentiment analysis prompts used with ChatGPT, and R code used for association rule mining. We have described the process of categorizing variables for reasoning analysis in more detail in the main text. We have also clarified in the manuscript that the SNS post data cannot be shared due to ethical and copyright concerns. However, we ensured that sufficient methodological details are provided to allow replication of our process using alternative datasets. Competing Interests: None. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 05 Sep 2025 Sunyoung Park , Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, 10444, South Korea 05 Sep 2025 Author Response 1. Literature Review: Reference to Argyris et al. : Thank you for the suggestion. We agree that the study by Argyris et al. provides important contextual support for our work. We ... Continue reading 1. Literature Review: Reference to Argyris et al. : Thank you for the suggestion. We agree that the study by Argyris et al. provides important contextual support for our work. We have revised the Literature Review section to include and discuss this reference. Specifically, we highlight how our study contributes to the growing body of research connecting social media sentiment with real-world vaccine behaviors, with a distinct focus on Korean-language data and the use of ChatGPT. 2. Methods – Use of Blind and Demographic Limitations: This is an important point. While we acknowledged this limitation, we agree that the methodological implications should be expanded. We have added a detailed discussion in both the Methods and Discussion sections, emphasizing how Blind’s focus on professionals may introduce bias, particularly regarding expressions of workplace-related pressure to vaccinate. We also discuss how company- and industry-specific groupings may affect discourse tone and topic. 3. Removal of Emojis in Preprocessing: We thank the reviewer for this valuable observation. We have now acknowledged in the Methods section that while emojis can contribute emotional context to sentiment analysis, they were removed to reduce preprocessing complexity and ensure consistency in textual inputs for the language model. We have also included a note in the Limitations section about the possible loss of emotional signals due to this decision. 4. Validation Sample Size and Coding Scheme: Thank you for this insightful comment. In response, we have expanded the validation sample from 100 to 200 posts to improve the reliability of our sentiment classification. The additional validation yielded a concordance rate of 84% between the two board-certified psychiatrists, which is consistent with the initial agreement rate. We have also provided a more detailed description of our coding scheme in the Methods section. Posts were categorized as positive, negative, or neutral based on the emotional tone and content related to COVID-19 vaccination. Annotation was guided by clinical criteria commonly used in psychiatric evaluation of affective expression, including linguistic cues (e.g., appraisal, sarcasm, urgency), expressed intentions (e.g., compliance, avoidance), and context (e.g., workplace influence, medical risk). Disagreements between annotators were resolved through discussion and consensus. These revisions are now reflected in the updated manuscript. 5. Figure 2 – Difficult Interpretation of Association Rules: Thank you for pointing this out. We have redesigned Figure 2 using a more intuitive layout and clearer legends. The caption has been expanded to explain what each element represents (support, confidence, lift), and we’ve added a narrative in the Results section that walks through key associations. 6. Discussion – Clarify the Tension Between Negative Sentiment and High Vaccination Rates: We fully agree. In the revised Discussion, we now highlight this discrepancy more explicitly. We discuss how high vaccine uptake in Korea may have been driven by workplace mandates or social expectations rather than positive sentiment, reinforcing the complex dynamics between public behavior and online discourse. 7. Reproducibility and Methodological Transparency: To enhance reproducibility, we have updated our GitHub repository to include the full set of preprocessing scripts, sentiment analysis prompts used with ChatGPT, and R code used for association rule mining. We have described the process of categorizing variables for reasoning analysis in more detail in the main text. We have also clarified in the manuscript that the SNS post data cannot be shared due to ethical and copyright concerns. However, we ensured that sufficient methodological details are provided to allow replication of our process using alternative datasets. 1. Literature Review: Reference to Argyris et al. : Thank you for the suggestion. We agree that the study by Argyris et al. provides important contextual support for our work. We have revised the Literature Review section to include and discuss this reference. Specifically, we highlight how our study contributes to the growing body of research connecting social media sentiment with real-world vaccine behaviors, with a distinct focus on Korean-language data and the use of ChatGPT. 2. Methods – Use of Blind and Demographic Limitations: This is an important point. While we acknowledged this limitation, we agree that the methodological implications should be expanded. We have added a detailed discussion in both the Methods and Discussion sections, emphasizing how Blind’s focus on professionals may introduce bias, particularly regarding expressions of workplace-related pressure to vaccinate. We also discuss how company- and industry-specific groupings may affect discourse tone and topic. 3. Removal of Emojis in Preprocessing: We thank the reviewer for this valuable observation. We have now acknowledged in the Methods section that while emojis can contribute emotional context to sentiment analysis, they were removed to reduce preprocessing complexity and ensure consistency in textual inputs for the language model. We have also included a note in the Limitations section about the possible loss of emotional signals due to this decision. 4. Validation Sample Size and Coding Scheme: Thank you for this insightful comment. In response, we have expanded the validation sample from 100 to 200 posts to improve the reliability of our sentiment classification. The additional validation yielded a concordance rate of 84% between the two board-certified psychiatrists, which is consistent with the initial agreement rate. We have also provided a more detailed description of our coding scheme in the Methods section. Posts were categorized as positive, negative, or neutral based on the emotional tone and content related to COVID-19 vaccination. Annotation was guided by clinical criteria commonly used in psychiatric evaluation of affective expression, including linguistic cues (e.g., appraisal, sarcasm, urgency), expressed intentions (e.g., compliance, avoidance), and context (e.g., workplace influence, medical risk). Disagreements between annotators were resolved through discussion and consensus. These revisions are now reflected in the updated manuscript. 5. Figure 2 – Difficult Interpretation of Association Rules: Thank you for pointing this out. We have redesigned Figure 2 using a more intuitive layout and clearer legends. The caption has been expanded to explain what each element represents (support, confidence, lift), and we’ve added a narrative in the Results section that walks through key associations. 6. Discussion – Clarify the Tension Between Negative Sentiment and High Vaccination Rates: We fully agree. In the revised Discussion, we now highlight this discrepancy more explicitly. We discuss how high vaccine uptake in Korea may have been driven by workplace mandates or social expectations rather than positive sentiment, reinforcing the complex dynamics between public behavior and online discourse. 7. Reproducibility and Methodological Transparency: To enhance reproducibility, we have updated our GitHub repository to include the full set of preprocessing scripts, sentiment analysis prompts used with ChatGPT, and R code used for association rule mining. We have described the process of categorizing variables for reasoning analysis in more detail in the main text. We have also clarified in the manuscript that the SNS post data cannot be shared due to ethical and copyright concerns. However, we ensured that sufficient methodological details are provided to allow replication of our process using alternative datasets. Competing Interests: None. Close Report a concern COMMENT ON THIS REPORT Version 2 VERSION 2 PUBLISHED 02 Sep 2024 Revised Views 0 Cite How to cite this report: Mohd Bahrin UF. Reviewer Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.169571.r336916 ) The direct URL for this report is: https://f1000research.com/articles/13-96/v2#referee-response-336916 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Nov 2024 Ummu Fatihah Mohd Bahrin , Universiti Teknologi MARA Cawangan Terengganu, Kuala Terengganu, Malaysia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.169571.r336916 The study notes that the sentiment analysis faced challenges due to the data imbalance, which led to overfitting, in detecting negative sentiments while reducing accuracy for positive feelings. However, the document does not elaborate on specific strategies to address the ... Continue reading READ ALL The study notes that the sentiment analysis faced challenges due to the data imbalance, which led to overfitting, in detecting negative sentiments while reducing accuracy for positive feelings. However, the document does not elaborate on specific strategies to address the imbalance. Recommendations for Handling Imbalanced Datasets Resampling Techniques: 1)Oversampling: Increase the number of positive and neutral posts using techniques like SMOTE (Synthetic Minority Oversampling Technique). 2)Undersampling: Reduce the number of negative posts to balance the dataset while ensuring that critical information is retained. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Setimnet Mining, machine learning, deep learning. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Mohd Bahrin UF. Reviewer Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.169571.r336916 ) The direct URL for this report is: https://f1000research.com/articles/13-96/v2#referee-response-336916 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 17 Jan 2025 Sunyoung Park , Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, 10444, South Korea 17 Jan 2025 Author Response Thank you so much for taking the time to review my manuscript and for providing such valuable and thoughtful comments. Your insights have been incredibly helpful and offered me a ... Continue reading Thank you so much for taking the time to review my manuscript and for providing such valuable and thoughtful comments. Your insights have been incredibly helpful and offered me a great opportunity to deepen my understanding of the topic. I truly appreciate the effort you put into making this a better piece of research. Following your comment, I considered several ways to fix the data imbalance and conducted additional analyses. I have also added the results and acknowledged this as a limitation in the Discussion section. 1. Limitations of Oversampling and Undersampling Undersampling : Since the total amount of data in this study is not sufficient, reducing the number of negative postings would lead to a lack of data for correlation analysis, which could undermine the reliability of the results. Therefore, undersampling was deemed unsuitable for this study. Oversampling : The recommended method, such as SMOTE, produces results in vector form, making it difficult to apply to sentiment analysis using language models like ChatGPT. For this reason, we attempted data augmentation using ChatGPT 1 . 2. Data Augmentation Data augmentation using ChatGPT was conducted with controlled prompts to encourage the use of synonyms and increase the diversity of positive and neutral data while maintaining contextual consistency. The augmented data was adjusted so that positive and neutral data accounted for 25-30% of the total dataset, with the following distribution after augmentation: Positive: Increased from 44 to 264 (220 added) Neutral: Increased from 103 to 295 (192 added) Negative: Maintained at 704 The augmented data was randomly mixed with the original data, forming the full dataset, and sentiment analysis was conducted again using ChatGPT. 3. Results of Sentiment Analysis After Data Augmentation Results : After data augmentation, the accuracy of the sentiment analysis did not improve. On the contrary, actual neutral data was more frequently misclassified as negative. Analysis of Causes : We propose the following hypotheses to explain these results: Nondeterministic Nature of ChatGPT : ChatGPT generates nondeterministic responses 2 , which may have led to the augmented minor dataset having no significant impact on the analysis results. This nondeterministic characteristic is particularly prone to inaccuracies in borderline cases (where the sentiment is ambiguous between positive and negative) and sarcasm. Quality Degradation in Augmented Data : The initial sentiment analysis accuracy was approximately 84%, suggesting possible inaccuracies in borderline cases and sarcastic expressions. If data augmentation was based on these inaccurate results, it likely generated lower-quality data that further reduced the overall dataset accuracy. The increase in cases where neutral data was misclassified as negative indicates potential challenges in accurately identifying ambiguous boundaries. References 1. Dai H, Liu Z, Liao W, Huang X, Cao Y, Wu Z, et al. Auggpt: Leveraging chatgpt for text data augmentation. arXiv. 2023; preprint arXiv:230213007. 10.48550/arXiv.2302.13007 2. Ouyang T, MaungMaung A, Konishi K, Seo Y, Echizen I. Stability analysis of chatgpt-based sentiment analysis in ai quality assurance. Electronics . 2024;13(24):5043. 10.3390/electronics13245043 Thank you so much for taking the time to review my manuscript and for providing such valuable and thoughtful comments. Your insights have been incredibly helpful and offered me a great opportunity to deepen my understanding of the topic. I truly appreciate the effort you put into making this a better piece of research. Following your comment, I considered several ways to fix the data imbalance and conducted additional analyses. I have also added the results and acknowledged this as a limitation in the Discussion section. 1. Limitations of Oversampling and Undersampling Undersampling : Since the total amount of data in this study is not sufficient, reducing the number of negative postings would lead to a lack of data for correlation analysis, which could undermine the reliability of the results. Therefore, undersampling was deemed unsuitable for this study. Oversampling : The recommended method, such as SMOTE, produces results in vector form, making it difficult to apply to sentiment analysis using language models like ChatGPT. For this reason, we attempted data augmentation using ChatGPT 1 . 2. Data Augmentation Data augmentation using ChatGPT was conducted with controlled prompts to encourage the use of synonyms and increase the diversity of positive and neutral data while maintaining contextual consistency. The augmented data was adjusted so that positive and neutral data accounted for 25-30% of the total dataset, with the following distribution after augmentation: Positive: Increased from 44 to 264 (220 added) Neutral: Increased from 103 to 295 (192 added) Negative: Maintained at 704 The augmented data was randomly mixed with the original data, forming the full dataset, and sentiment analysis was conducted again using ChatGPT. 3. Results of Sentiment Analysis After Data Augmentation Results : After data augmentation, the accuracy of the sentiment analysis did not improve. On the contrary, actual neutral data was more frequently misclassified as negative. Analysis of Causes : We propose the following hypotheses to explain these results: Nondeterministic Nature of ChatGPT : ChatGPT generates nondeterministic responses 2 , which may have led to the augmented minor dataset having no significant impact on the analysis results. This nondeterministic characteristic is particularly prone to inaccuracies in borderline cases (where the sentiment is ambiguous between positive and negative) and sarcasm. Quality Degradation in Augmented Data : The initial sentiment analysis accuracy was approximately 84%, suggesting possible inaccuracies in borderline cases and sarcastic expressions. If data augmentation was based on these inaccurate results, it likely generated lower-quality data that further reduced the overall dataset accuracy. The increase in cases where neutral data was misclassified as negative indicates potential challenges in accurately identifying ambiguous boundaries. References 1. Dai H, Liu Z, Liao W, Huang X, Cao Y, Wu Z, et al. Auggpt: Leveraging chatgpt for text data augmentation. arXiv. 2023; preprint arXiv:230213007. 10.48550/arXiv.2302.13007 2. Ouyang T, MaungMaung A, Konishi K, Seo Y, Echizen I. Stability analysis of chatgpt-based sentiment analysis in ai quality assurance. Electronics . 2024;13(24):5043. 10.3390/electronics13245043 Competing Interests: None Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 17 Jan 2025 Sunyoung Park , Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, 10444, South Korea 17 Jan 2025 Author Response Thank you so much for taking the time to review my manuscript and for providing such valuable and thoughtful comments. Your insights have been incredibly helpful and offered me a ... Continue reading Thank you so much for taking the time to review my manuscript and for providing such valuable and thoughtful comments. Your insights have been incredibly helpful and offered me a great opportunity to deepen my understanding of the topic. I truly appreciate the effort you put into making this a better piece of research. Following your comment, I considered several ways to fix the data imbalance and conducted additional analyses. I have also added the results and acknowledged this as a limitation in the Discussion section. 1. Limitations of Oversampling and Undersampling Undersampling : Since the total amount of data in this study is not sufficient, reducing the number of negative postings would lead to a lack of data for correlation analysis, which could undermine the reliability of the results. Therefore, undersampling was deemed unsuitable for this study. Oversampling : The recommended method, such as SMOTE, produces results in vector form, making it difficult to apply to sentiment analysis using language models like ChatGPT. For this reason, we attempted data augmentation using ChatGPT 1 . 2. Data Augmentation Data augmentation using ChatGPT was conducted with controlled prompts to encourage the use of synonyms and increase the diversity of positive and neutral data while maintaining contextual consistency. The augmented data was adjusted so that positive and neutral data accounted for 25-30% of the total dataset, with the following distribution after augmentation: Positive: Increased from 44 to 264 (220 added) Neutral: Increased from 103 to 295 (192 added) Negative: Maintained at 704 The augmented data was randomly mixed with the original data, forming the full dataset, and sentiment analysis was conducted again using ChatGPT. 3. Results of Sentiment Analysis After Data Augmentation Results : After data augmentation, the accuracy of the sentiment analysis did not improve. On the contrary, actual neutral data was more frequently misclassified as negative. Analysis of Causes : We propose the following hypotheses to explain these results: Nondeterministic Nature of ChatGPT : ChatGPT generates nondeterministic responses 2 , which may have led to the augmented minor dataset having no significant impact on the analysis results. This nondeterministic characteristic is particularly prone to inaccuracies in borderline cases (where the sentiment is ambiguous between positive and negative) and sarcasm. Quality Degradation in Augmented Data : The initial sentiment analysis accuracy was approximately 84%, suggesting possible inaccuracies in borderline cases and sarcastic expressions. If data augmentation was based on these inaccurate results, it likely generated lower-quality data that further reduced the overall dataset accuracy. The increase in cases where neutral data was misclassified as negative indicates potential challenges in accurately identifying ambiguous boundaries. References 1. Dai H, Liu Z, Liao W, Huang X, Cao Y, Wu Z, et al. Auggpt: Leveraging chatgpt for text data augmentation. arXiv. 2023; preprint arXiv:230213007. 10.48550/arXiv.2302.13007 2. Ouyang T, MaungMaung A, Konishi K, Seo Y, Echizen I. Stability analysis of chatgpt-based sentiment analysis in ai quality assurance. Electronics . 2024;13(24):5043. 10.3390/electronics13245043 Thank you so much for taking the time to review my manuscript and for providing such valuable and thoughtful comments. Your insights have been incredibly helpful and offered me a great opportunity to deepen my understanding of the topic. I truly appreciate the effort you put into making this a better piece of research. Following your comment, I considered several ways to fix the data imbalance and conducted additional analyses. I have also added the results and acknowledged this as a limitation in the Discussion section. 1. Limitations of Oversampling and Undersampling Undersampling : Since the total amount of data in this study is not sufficient, reducing the number of negative postings would lead to a lack of data for correlation analysis, which could undermine the reliability of the results. Therefore, undersampling was deemed unsuitable for this study. Oversampling : The recommended method, such as SMOTE, produces results in vector form, making it difficult to apply to sentiment analysis using language models like ChatGPT. For this reason, we attempted data augmentation using ChatGPT 1 . 2. Data Augmentation Data augmentation using ChatGPT was conducted with controlled prompts to encourage the use of synonyms and increase the diversity of positive and neutral data while maintaining contextual consistency. The augmented data was adjusted so that positive and neutral data accounted for 25-30% of the total dataset, with the following distribution after augmentation: Positive: Increased from 44 to 264 (220 added) Neutral: Increased from 103 to 295 (192 added) Negative: Maintained at 704 The augmented data was randomly mixed with the original data, forming the full dataset, and sentiment analysis was conducted again using ChatGPT. 3. Results of Sentiment Analysis After Data Augmentation Results : After data augmentation, the accuracy of the sentiment analysis did not improve. On the contrary, actual neutral data was more frequently misclassified as negative. Analysis of Causes : We propose the following hypotheses to explain these results: Nondeterministic Nature of ChatGPT : ChatGPT generates nondeterministic responses 2 , which may have led to the augmented minor dataset having no significant impact on the analysis results. This nondeterministic characteristic is particularly prone to inaccuracies in borderline cases (where the sentiment is ambiguous between positive and negative) and sarcasm. Quality Degradation in Augmented Data : The initial sentiment analysis accuracy was approximately 84%, suggesting possible inaccuracies in borderline cases and sarcastic expressions. If data augmentation was based on these inaccurate results, it likely generated lower-quality data that further reduced the overall dataset accuracy. The increase in cases where neutral data was misclassified as negative indicates potential challenges in accurately identifying ambiguous boundaries. References 1. Dai H, Liu Z, Liao W, Huang X, Cao Y, Wu Z, et al. Auggpt: Leveraging chatgpt for text data augmentation. arXiv. 2023; preprint arXiv:230213007. 10.48550/arXiv.2302.13007 2. Ouyang T, MaungMaung A, Konishi K, Seo Y, Echizen I. Stability analysis of chatgpt-based sentiment analysis in ai quality assurance. Electronics . 2024;13(24):5043. 10.3390/electronics13245043 Competing Interests: None Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Cilgin C. Reviewer Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.169571.r319806 ) The direct URL for this report is: https://f1000research.com/articles/13-96/v2#referee-response-319806 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 04 Sep 2024 Cihan Cilgin , Bolu Abant Izzet Baysal University, Bolu, Turkey Approved VIEWS 0 https://doi.org/10.5256/f1000research.169571.r319806 I think it is appropriate to approve and ... Continue reading READ ALL I think it is appropriate to approve and index this study in its current, final form. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Machine Learning, Deep Learning, sentiment analysis, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Cilgin C. Reviewer Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.169571.r319806 ) The direct URL for this report is: https://f1000research.com/articles/13-96/v2#referee-response-319806 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 15 Feb 2024 Views 0 Cite How to cite this report: Cilgin C. Reviewer Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.159851.r284069 ) The direct URL for this report is: https://f1000research.com/articles/13-96/v1#referee-response-284069 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 17 Jun 2024 Cihan Cilgin , Bolu Abant Izzet Baysal University, Bolu, Turkey Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.159851.r284069 This study used ChatGPT to perform sentiment analysis on posts about Covid-19 vaccines. 1. Within the scope of the study, many studies conducted within the same scope were ignored and the literature section was not given. However, the ... Continue reading READ ALL This study used ChatGPT to perform sentiment analysis on posts about Covid-19 vaccines. 1. Within the scope of the study, many studies conducted within the same scope were ignored and the literature section was not given. However, the existing studies in the literature are very important to both reveal and support the current contributions of these studies. For example, the study given below is a direct alternative to this issue: >> Çılgın, C., Gökçen, H., & Gökşen, Y. (2023 [Ref -1]). Sentiment analysis of public sensitivity to COVID-19 vaccines on Twitter by majority voting classifier-based machine learning. Journal of the Faculty of Engineering and Architecture of Gazi University , 38 (2) 2. There are many repetitive sentences under the title of web crawling and data collection. This title should definitely be reviewed again. 3. It is really interesting that only 854 relevant posts were confiscated out of approximately 500 thousand posts. Many studies in the literature used social media posts much higher than this number. The reader should be made more aware here by giving more details about this filtering process. 4. Using a lexicon-based approach such as Vader or TextBlob in sentiment analysis, in addition to ChatGPT, may make the results of this study more valuable. In addition, a suitable data set is available for such a method comparison. 5. Presenting a confusion matrix of the results obtained as a result of the 100 posts considered for the test data set is very important in terms of the consistency of the results. 6. There are repetitive expressions, especially in the Discussion section of the study. 7. There is no statement in the Discussion section that compares the findings of this study with the findings of other studies in the literature. As mentioned before, this is related to the lack of literature section. The similarity or difference of the findings with the existing literature is very important for the reader to evaluate the findings of this study. This study used ChatGPT to perform sentiment analysis on posts about Covid-19 vaccines. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes References 1. ÇILGIN C, GÖKÇEN H, GÖKŞEN Y: Twitter’da COVID-19 aşılarına karşı kamu duyarlılığının çoğunluk oylama sınıflandırıcısı temelli makine öğrenmesi ile duygu analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi . 2022; 38 (2): 1093-1104 Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Machine Learning, Deep Learning, sentiment analysis, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Cilgin C. Reviewer Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.159851.r284069 ) The direct URL for this report is: https://f1000research.com/articles/13-96/v1#referee-response-284069 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 02 Sep 2024 Sunyoung Park , Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, 10444, South Korea 02 Sep 2024 Author Response Thank you for your comment. I have revised this article as below. Please go through the respective Author responses for Reviewer Comments made. 1. As suggested, I have developed ... Continue reading Thank you for your comment. I have revised this article as below. Please go through the respective Author responses for Reviewer Comments made. 1. As suggested, I have developed and included a comprehensive literature review related to the main topic of this study. Additionally, I have incorporated important insights from the paper you mentioned as an example, which have significantly contributed to the argument presented in my work. 2. Thank you for pointing this out. There was an error in the editing process of the Methods section, which led to the repetition of content. This has now been corrected. 3. Thank you for highlighting this critical point. The relatively small number of relevant posts collected in this study can be attributed to the characteristics of the website BLIND, from which the data was gathered, and the fact that data collection occurred some time after vaccines were a hot topic. I have added a detailed explanation to address this. Additionally, I have also enhanced and elaborated on the filtering process in the study. 4. Thank you for your insightful suggestion. It is true that most sentiment analysis studies utilize English data, and the lexicons you mentioned are indeed based on English. However, Korean, being a less commonly used language globally, presents challenges in applying these pre-established analytic methods directly. I believe this underscores the significance of using ChatGPT in our study, which is specifically adapted to handle Korean text. This point has been detailed in the literature review and discussed further in the discussion section of our paper. 5. I have added the results of the confusion matrix and included comments on the identified features in the discussion section. Additionally, while creating the confusion matrix, there were slight modifications to the previously manually evaluated accuracy results (from 86% to 85%). 6. I have revised the Discussion section to eliminate repetitive expressions and streamline the content. 7. Thank you for your meaningful feedback. Our study has notable differences from other studies, particularly due to the linguistic and sociocultural factors specific to Korea. I have addressed these differences and added a comparison of our findings with those in the existing literature in the Discussion section. Thank you for your comment. I have revised this article as below. Please go through the respective Author responses for Reviewer Comments made. 1. As suggested, I have developed and included a comprehensive literature review related to the main topic of this study. Additionally, I have incorporated important insights from the paper you mentioned as an example, which have significantly contributed to the argument presented in my work. 2. Thank you for pointing this out. There was an error in the editing process of the Methods section, which led to the repetition of content. This has now been corrected. 3. Thank you for highlighting this critical point. The relatively small number of relevant posts collected in this study can be attributed to the characteristics of the website BLIND, from which the data was gathered, and the fact that data collection occurred some time after vaccines were a hot topic. I have added a detailed explanation to address this. Additionally, I have also enhanced and elaborated on the filtering process in the study. 4. Thank you for your insightful suggestion. It is true that most sentiment analysis studies utilize English data, and the lexicons you mentioned are indeed based on English. However, Korean, being a less commonly used language globally, presents challenges in applying these pre-established analytic methods directly. I believe this underscores the significance of using ChatGPT in our study, which is specifically adapted to handle Korean text. This point has been detailed in the literature review and discussed further in the discussion section of our paper. 5. I have added the results of the confusion matrix and included comments on the identified features in the discussion section. Additionally, while creating the confusion matrix, there were slight modifications to the previously manually evaluated accuracy results (from 86% to 85%). 6. I have revised the Discussion section to eliminate repetitive expressions and streamline the content. 7. Thank you for your meaningful feedback. Our study has notable differences from other studies, particularly due to the linguistic and sociocultural factors specific to Korea. I have addressed these differences and added a comparison of our findings with those in the existing literature in the Discussion section. Competing Interests: None Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 02 Sep 2024 Sunyoung Park , Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, 10444, South Korea 02 Sep 2024 Author Response Thank you for your comment. I have revised this article as below. Please go through the respective Author responses for Reviewer Comments made. 1. As suggested, I have developed ... Continue reading Thank you for your comment. I have revised this article as below. Please go through the respective Author responses for Reviewer Comments made. 1. As suggested, I have developed and included a comprehensive literature review related to the main topic of this study. Additionally, I have incorporated important insights from the paper you mentioned as an example, which have significantly contributed to the argument presented in my work. 2. Thank you for pointing this out. There was an error in the editing process of the Methods section, which led to the repetition of content. This has now been corrected. 3. Thank you for highlighting this critical point. The relatively small number of relevant posts collected in this study can be attributed to the characteristics of the website BLIND, from which the data was gathered, and the fact that data collection occurred some time after vaccines were a hot topic. I have added a detailed explanation to address this. Additionally, I have also enhanced and elaborated on the filtering process in the study. 4. Thank you for your insightful suggestion. It is true that most sentiment analysis studies utilize English data, and the lexicons you mentioned are indeed based on English. However, Korean, being a less commonly used language globally, presents challenges in applying these pre-established analytic methods directly. I believe this underscores the significance of using ChatGPT in our study, which is specifically adapted to handle Korean text. This point has been detailed in the literature review and discussed further in the discussion section of our paper. 5. I have added the results of the confusion matrix and included comments on the identified features in the discussion section. Additionally, while creating the confusion matrix, there were slight modifications to the previously manually evaluated accuracy results (from 86% to 85%). 6. I have revised the Discussion section to eliminate repetitive expressions and streamline the content. 7. Thank you for your meaningful feedback. Our study has notable differences from other studies, particularly due to the linguistic and sociocultural factors specific to Korea. I have addressed these differences and added a comparison of our findings with those in the existing literature in the Discussion section. Thank you for your comment. I have revised this article as below. Please go through the respective Author responses for Reviewer Comments made. 1. As suggested, I have developed and included a comprehensive literature review related to the main topic of this study. Additionally, I have incorporated important insights from the paper you mentioned as an example, which have significantly contributed to the argument presented in my work. 2. Thank you for pointing this out. There was an error in the editing process of the Methods section, which led to the repetition of content. This has now been corrected. 3. Thank you for highlighting this critical point. The relatively small number of relevant posts collected in this study can be attributed to the characteristics of the website BLIND, from which the data was gathered, and the fact that data collection occurred some time after vaccines were a hot topic. I have added a detailed explanation to address this. Additionally, I have also enhanced and elaborated on the filtering process in the study. 4. Thank you for your insightful suggestion. It is true that most sentiment analysis studies utilize English data, and the lexicons you mentioned are indeed based on English. However, Korean, being a less commonly used language globally, presents challenges in applying these pre-established analytic methods directly. I believe this underscores the significance of using ChatGPT in our study, which is specifically adapted to handle Korean text. This point has been detailed in the literature review and discussed further in the discussion section of our paper. 5. I have added the results of the confusion matrix and included comments on the identified features in the discussion section. Additionally, while creating the confusion matrix, there were slight modifications to the previously manually evaluated accuracy results (from 86% to 85%). 6. I have revised the Discussion section to eliminate repetitive expressions and streamline the content. 7. Thank you for your meaningful feedback. Our study has notable differences from other studies, particularly due to the linguistic and sociocultural factors specific to Korea. I have addressed these differences and added a comparison of our findings with those in the existing literature in the Discussion section. Competing Interests: None Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 4 VERSION 4 PUBLISHED 15 Feb 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 4 (revision) 24 Sep 25 read Version 3 (revision) 17 Jan 25 read Version 2 (revision) 02 Sep 24 read read Version 1 15 Feb 24 read Cihan Cilgin , Bolu Abant Izzet Baysal University, Bolu, Turkey Ummu Fatihah Mohd Bahrin , Universiti Teknologi MARA Cawangan Terengganu, Kuala Terengganu, Malaysia Young Anna Argyris , Michigan State University, East Lansing, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Mohd Bahrin U. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 14 Oct 2025 | for Version 4 Ummu Fatihah Mohd Bahrin , Universiti Teknologi MARA Cawangan Terengganu, Kuala Terengganu, Malaysia 0 Views copyright © 2025 Mohd Bahrin U. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I have carefully gone through the revisions made, and I am satisfied with the improvements. The authors have sufficiently addressed my previous comments, and I have no further reservations. Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Mohd Bahrin UF. Peer Review Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.186240.r417210) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-96/v4#referee-response-417210 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Argyris Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 19 Jun 2025 | for Version 3 Young Anna Argyris , Michigan State University, East Lansing, Michigan, USA 0 Views copyright © 2025 Argyris Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Overview This paper addresses a crucial issue—public sentiment toward COVID-19 vaccination in South Korea—and examines it through a novel approach: utilizing ChatGPT-based sentiment analysis on social media data. The study's attempt to connect sentiment trends to real-world vaccination rates is timely and commendable, and the use of advanced language models is a noteworthy methodological innovation. However, the manuscript has several substantive issues that prevent it from being indexed in its current form. While I highly appreciate the authors’ effort and believe this line of inquiry is valuable, the concerns outlined below—including methodological limitations and unclear contributions —need to be addressed more fully. I share these critiques in the hope that they will help the authors improve their paper. Literature Review The authors may also consider referencing a similar approach in Argyris et al.'s study published in Social Science & Medicine, which also used natural language processing to analyze online vaccine discourse: (Ref.1). They also demonstrate significant correlations between social media posts and vaccination rates, and elaborate potential reasons for the associations, which might be helpful for the authors to contextualize the contribution of this paper within the broader literature. Methods Note: Other reviewers have already addressed other issues, such as data imbalance and small sample size, and are not reiterated here. The authors specify that posts were collected from Blind, an anonymous social media community targeted at working professionals. While this is helpful, the implications of using a niche platform with a specific user demographic should be discussed more thoroughly, particularly regarding the representativeness of the data, ethical considerations related to consent and privacy, and the generalizability of the study’s findings. To their credit, the authors do acknowledge this limitation in the discussion section, noting that the Blind platform primarily targets professionals and may not capture broader public sentiment. However, merely acknowledging this limitation does not fully address the methodological implications. The restricted demographic may systematically bias the types of sentiments expressed, especially given that workplace pressures and professional norms likely influence how vaccination is discussed on the platform. Blind is organized into numerous company- and industry-specific groups, which may affect the topics and tone of discussions, as well as amplify particular workplace-related sentiments such as coercion or pressure to comply with vaccination mandates. The posts underwent text preprocessing, which involved UTF-8 encoding, removal of stop words and URLs, as well as removal of emojis and special characters. While such cleaning is common, the removal of emojis may be questionable, as emojis—though not crucial—can be useful for sentiment analysis and emotional nuance detection. The authors should justify this decision or acknowledge its potential implications for the analysis. A major concern is the reliability of ChatGPT in accurately classifying sentiment in the analyzed posts. The authors report an 85% agreement rate between the model and two psychiatrists, but this evaluation was conducted on only 100 posts out of 854 analyzed. Given the complexity and nuance of language in social media discourse—particularly when dealing with sarcasm, irony, or emotionally charged content—a validation sample of 100 posts is insufficient to establish confidence in the model's overall performance. A more rigorous evaluation, ideally with interrater comparisons across a larger and more diverse subset of posts, is necessary to support the claim that the model accurately reflects user sentiment. The authors should clarify the coding scheme. Results and Visualization Figure 2 is very difficult to understand. Based on the accompanying description, the authors appear to be using association rule mining to identify co-occurring sentiment categories within the reasons given for or against vaccination. For example, they report support, confidence, and lift values to indicate the strength and reliability of relationships like “Decreasing mortality rate” being associated with “Symptom alleviation,” or “Antipathy to social oppression” with “Mistrust.” However, the figure itself lacks clear labels, an intuitive layout, and an adequate explanation in the caption. The connection between the visual elements and these statistical measures is not readily apparent. The figure would benefit from a simplified design, a clearer legend, and a more detailed narrative in the main text to help readers accurately interpret the findings. For guidance on more effective visualizations of association rule mining results, the authors may refer to Dolores et al. (2023) - (Ref 2). Table 4, which presents a clear and practical summary of association rule metrics and their interpretations. Discussion Interestingly, the study finds a strong correlation between the number of negative posts and actual vaccination rates, despite the overwhelmingly negative sentiment expressed in the data. The authors acknowledge that many individuals were vaccinated not out of conviction, but because of job requirements or social pressure. In this context, the findings suggest that high vaccination rates in Korea (reported at 96.9%) may reflect compliance driven by external pressures rather than positive public sentiment. This nuance is important: public health behavior may not align with expressed beliefs, particularly in environments where vaccination is perceived as socially or professionally obligatory. The authors could emphasize this tension more clearly to avoid the mistaken interpretation that high uptake equates to high trust or positive attitudes toward vaccines. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? No References 1. Nelson V, Bashyal B, Tan P, Argyris Y: Vaccine rhetoric on social media and COVID-19 vaccine uptake rates: A triangulation using self-reported vaccine acceptance. Social Science & Medicine . 2024; 348 . Publisher Full Text 2. Dolores M, Fernandez-Basso C, Gómez-Romero J, Martin-Bautista M: A big data association rule mining based approach for energy building behaviour analysis in an IoT environment. Scientific Reports . 2023; 13 (1). Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Information Systems, Health Communication, Computational method I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 05 Sep 2025 Sunyoung Park, Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, 10444, South Korea 1. Literature Review: Reference to Argyris et al. : Thank you for the suggestion. We agree that the study by Argyris et al. provides important contextual support for our work. We have revised the Literature Review section to include and discuss this reference. Specifically, we highlight how our study contributes to the growing body of research connecting social media sentiment with real-world vaccine behaviors, with a distinct focus on Korean-language data and the use of ChatGPT. 2. Methods – Use of Blind and Demographic Limitations: This is an important point. While we acknowledged this limitation, we agree that the methodological implications should be expanded. We have added a detailed discussion in both the Methods and Discussion sections, emphasizing how Blind’s focus on professionals may introduce bias, particularly regarding expressions of workplace-related pressure to vaccinate. We also discuss how company- and industry-specific groupings may affect discourse tone and topic. 3. Removal of Emojis in Preprocessing: We thank the reviewer for this valuable observation. We have now acknowledged in the Methods section that while emojis can contribute emotional context to sentiment analysis, they were removed to reduce preprocessing complexity and ensure consistency in textual inputs for the language model. We have also included a note in the Limitations section about the possible loss of emotional signals due to this decision. 4. Validation Sample Size and Coding Scheme: Thank you for this insightful comment. In response, we have expanded the validation sample from 100 to 200 posts to improve the reliability of our sentiment classification. The additional validation yielded a concordance rate of 84% between the two board-certified psychiatrists, which is consistent with the initial agreement rate. We have also provided a more detailed description of our coding scheme in the Methods section. Posts were categorized as positive, negative, or neutral based on the emotional tone and content related to COVID-19 vaccination. Annotation was guided by clinical criteria commonly used in psychiatric evaluation of affective expression, including linguistic cues (e.g., appraisal, sarcasm, urgency), expressed intentions (e.g., compliance, avoidance), and context (e.g., workplace influence, medical risk). Disagreements between annotators were resolved through discussion and consensus. These revisions are now reflected in the updated manuscript. 5. Figure 2 – Difficult Interpretation of Association Rules: Thank you for pointing this out. We have redesigned Figure 2 using a more intuitive layout and clearer legends. The caption has been expanded to explain what each element represents (support, confidence, lift), and we’ve added a narrative in the Results section that walks through key associations. 6. Discussion – Clarify the Tension Between Negative Sentiment and High Vaccination Rates: We fully agree. In the revised Discussion, we now highlight this discrepancy more explicitly. We discuss how high vaccine uptake in Korea may have been driven by workplace mandates or social expectations rather than positive sentiment, reinforcing the complex dynamics between public behavior and online discourse. 7. Reproducibility and Methodological Transparency: To enhance reproducibility, we have updated our GitHub repository to include the full set of preprocessing scripts, sentiment analysis prompts used with ChatGPT, and R code used for association rule mining. We have described the process of categorizing variables for reasoning analysis in more detail in the main text. We have also clarified in the manuscript that the SNS post data cannot be shared due to ethical and copyright concerns. However, we ensured that sufficient methodological details are provided to allow replication of our process using alternative datasets. View more View less Competing Interests None. reply Respond Report a concern Argyris YA. Peer Review Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.176895.r382353) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-96/v3#referee-response-382353 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Mohd Bahrin U. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Nov 2024 | for Version 2 Ummu Fatihah Mohd Bahrin , Universiti Teknologi MARA Cawangan Terengganu, Kuala Terengganu, Malaysia 0 Views copyright © 2024 Mohd Bahrin U. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The study notes that the sentiment analysis faced challenges due to the data imbalance, which led to overfitting, in detecting negative sentiments while reducing accuracy for positive feelings. However, the document does not elaborate on specific strategies to address the imbalance. Recommendations for Handling Imbalanced Datasets Resampling Techniques: 1)Oversampling: Increase the number of positive and neutral posts using techniques like SMOTE (Synthetic Minority Oversampling Technique). 2)Undersampling: Reduce the number of negative posts to balance the dataset while ensuring that critical information is retained. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Setimnet Mining, machine learning, deep learning. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 17 Jan 2025 Sunyoung Park, Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, 10444, South Korea Thank you so much for taking the time to review my manuscript and for providing such valuable and thoughtful comments. Your insights have been incredibly helpful and offered me a great opportunity to deepen my understanding of the topic. I truly appreciate the effort you put into making this a better piece of research. Following your comment, I considered several ways to fix the data imbalance and conducted additional analyses. I have also added the results and acknowledged this as a limitation in the Discussion section. 1. Limitations of Oversampling and Undersampling Undersampling : Since the total amount of data in this study is not sufficient, reducing the number of negative postings would lead to a lack of data for correlation analysis, which could undermine the reliability of the results. Therefore, undersampling was deemed unsuitable for this study. Oversampling : The recommended method, such as SMOTE, produces results in vector form, making it difficult to apply to sentiment analysis using language models like ChatGPT. For this reason, we attempted data augmentation using ChatGPT 1 . 2. Data Augmentation Data augmentation using ChatGPT was conducted with controlled prompts to encourage the use of synonyms and increase the diversity of positive and neutral data while maintaining contextual consistency. The augmented data was adjusted so that positive and neutral data accounted for 25-30% of the total dataset, with the following distribution after augmentation: Positive: Increased from 44 to 264 (220 added) Neutral: Increased from 103 to 295 (192 added) Negative: Maintained at 704 The augmented data was randomly mixed with the original data, forming the full dataset, and sentiment analysis was conducted again using ChatGPT. 3. Results of Sentiment Analysis After Data Augmentation Results : After data augmentation, the accuracy of the sentiment analysis did not improve. On the contrary, actual neutral data was more frequently misclassified as negative. Analysis of Causes : We propose the following hypotheses to explain these results: Nondeterministic Nature of ChatGPT : ChatGPT generates nondeterministic responses 2 , which may have led to the augmented minor dataset having no significant impact on the analysis results. This nondeterministic characteristic is particularly prone to inaccuracies in borderline cases (where the sentiment is ambiguous between positive and negative) and sarcasm. Quality Degradation in Augmented Data : The initial sentiment analysis accuracy was approximately 84%, suggesting possible inaccuracies in borderline cases and sarcastic expressions. If data augmentation was based on these inaccurate results, it likely generated lower-quality data that further reduced the overall dataset accuracy. The increase in cases where neutral data was misclassified as negative indicates potential challenges in accurately identifying ambiguous boundaries. References 1. Dai H, Liu Z, Liao W, Huang X, Cao Y, Wu Z, et al. Auggpt: Leveraging chatgpt for text data augmentation. arXiv. 2023; preprint arXiv:230213007. 10.48550/arXiv.2302.13007 2. Ouyang T, MaungMaung A, Konishi K, Seo Y, Echizen I. Stability analysis of chatgpt-based sentiment analysis in ai quality assurance. Electronics . 2024;13(24):5043. 10.3390/electronics13245043 View more View less Competing Interests None reply Respond Report a concern Mohd Bahrin UF. Peer Review Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.169571.r336916) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-96/v2#referee-response-336916 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Cilgin C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 04 Sep 2024 | for Version 2 Cihan Cilgin , Bolu Abant Izzet Baysal University, Bolu, Turkey 0 Views copyright © 2024 Cilgin C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I think it is appropriate to approve and index this study in its current, final form. Competing Interests No competing interests were disclosed. Reviewer Expertise Machine Learning, Deep Learning, sentiment analysis, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Cilgin C. Peer Review Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.169571.r319806) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-96/v2#referee-response-319806 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Cilgin C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 17 Jun 2024 | for Version 1 Cihan Cilgin , Bolu Abant Izzet Baysal University, Bolu, Turkey 0 Views copyright © 2024 Cilgin C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This study used ChatGPT to perform sentiment analysis on posts about Covid-19 vaccines. 1. Within the scope of the study, many studies conducted within the same scope were ignored and the literature section was not given. However, the existing studies in the literature are very important to both reveal and support the current contributions of these studies. For example, the study given below is a direct alternative to this issue: >> Çılgın, C., Gökçen, H., & Gökşen, Y. (2023 [Ref -1]). Sentiment analysis of public sensitivity to COVID-19 vaccines on Twitter by majority voting classifier-based machine learning. Journal of the Faculty of Engineering and Architecture of Gazi University , 38 (2) 2. There are many repetitive sentences under the title of web crawling and data collection. This title should definitely be reviewed again. 3. It is really interesting that only 854 relevant posts were confiscated out of approximately 500 thousand posts. Many studies in the literature used social media posts much higher than this number. The reader should be made more aware here by giving more details about this filtering process. 4. Using a lexicon-based approach such as Vader or TextBlob in sentiment analysis, in addition to ChatGPT, may make the results of this study more valuable. In addition, a suitable data set is available for such a method comparison. 5. Presenting a confusion matrix of the results obtained as a result of the 100 posts considered for the test data set is very important in terms of the consistency of the results. 6. There are repetitive expressions, especially in the Discussion section of the study. 7. There is no statement in the Discussion section that compares the findings of this study with the findings of other studies in the literature. As mentioned before, this is related to the lack of literature section. The similarity or difference of the findings with the existing literature is very important for the reader to evaluate the findings of this study. This study used ChatGPT to perform sentiment analysis on posts about Covid-19 vaccines. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes References 1. ÇILGIN C, GÖKÇEN H, GÖKŞEN Y: Twitter’da COVID-19 aşılarına karşı kamu duyarlılığının çoğunluk oylama sınıflandırıcısı temelli makine öğrenmesi ile duygu analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi . 2022; 38 (2): 1093-1104 Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Machine Learning, Deep Learning, sentiment analysis, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 02 Sep 2024 Sunyoung Park, Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, 10444, South Korea Thank you for your comment. I have revised this article as below. Please go through the respective Author responses for Reviewer Comments made. 1. As suggested, I have developed and included a comprehensive literature review related to the main topic of this study. Additionally, I have incorporated important insights from the paper you mentioned as an example, which have significantly contributed to the argument presented in my work. 2. Thank you for pointing this out. There was an error in the editing process of the Methods section, which led to the repetition of content. This has now been corrected. 3. Thank you for highlighting this critical point. The relatively small number of relevant posts collected in this study can be attributed to the characteristics of the website BLIND, from which the data was gathered, and the fact that data collection occurred some time after vaccines were a hot topic. I have added a detailed explanation to address this. Additionally, I have also enhanced and elaborated on the filtering process in the study. 4. Thank you for your insightful suggestion. It is true that most sentiment analysis studies utilize English data, and the lexicons you mentioned are indeed based on English. However, Korean, being a less commonly used language globally, presents challenges in applying these pre-established analytic methods directly. I believe this underscores the significance of using ChatGPT in our study, which is specifically adapted to handle Korean text. This point has been detailed in the literature review and discussed further in the discussion section of our paper. 5. I have added the results of the confusion matrix and included comments on the identified features in the discussion section. Additionally, while creating the confusion matrix, there were slight modifications to the previously manually evaluated accuracy results (from 86% to 85%). 6. I have revised the Discussion section to eliminate repetitive expressions and streamline the content. 7. Thank you for your meaningful feedback. Our study has notable differences from other studies, particularly due to the linguistic and sociocultural factors specific to Korea. I have addressed these differences and added a comparison of our findings with those in the existing literature in the Discussion section. View more View less Competing Interests None reply Respond Report a concern Cilgin C. Peer Review Report For: Sentiment analysis of internet posts on vaccination using ChatGPT and comparison with actual vaccination rates in South Korea [version 4; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 13 :96 ( https://doi.org/10.5256/f1000research.159851.r284069) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-96/v1#referee-response-284069 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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