Background: The COVID-19 pandemic prompted global behavioral restrictions, impacting public mental health. Sentiment analysis, a tool for assessing individual and public emotions from text data, gained importance amid the pandemic. This study focuses on Japan’s early public health interventions during COVID-19, utilizing sentiment analysis in infodemiology to gauge public sentiment on social media regarding these interventions. Objective: This study aims to investigate shifts in public emotions and sentiments before and after the first state of emergency was declared in Japan. By analyzing both user-generated tweets and retweets, we aim to discern patterns in emotional responses during this critical period. Methods: We conducted a day-by-day analysis of Twitter (now known as X) data using 4,894,009 tweets containing the keywords “corona,” “COVID-19,” and “new pneumonia” from March 23 to April 21, 2020, approximately 2 weeks before and after the first declaration of a state of emergency in Japan. We also processed tweet data into vectors for each word, employing the Fuzzy-C-Means (FCM) method, a type of cluster analysis, for the words in the sentiment dictionary. We set up 7 sentiment clusters (negative: anger, sadness, surprise, disgust; neutral: anxiety; positive: trust and joy) and conducted sentiment analysis of the tweet groups and retweet groups. Results: The analysis revealed a mix of positive and negative sentiments, with “joy” significantly increasing in the retweet group after the state of emergency declaration. Negative emotions, such as “worry” and “disgust,” were prevalent in both tweet and retweet groups. Furthermore, the retweet group had a tendency to share more negative content compared to the tweet group. Conclusions: This study conducted sentiment analysis of Japanese tweets and retweets to explore public sentiments during the early stages of COVID-19 in Japan, spanning 2 weeks before and after the first state of emergency declaration. The analysis revealed a mix of positive (joy) and negative (anxiety, disgust) emotions. Notably, joy increased in the retweet group after the emergency declaration, but this group also tended to share more negative content than the tweet group. This study suggests that the state of emergency heightened positive sentiments due to expectations for infection prevention measures, yet negative information also gained traction. The findings propose the potential for further exploration through network analysis.

Biomedical terms extracted using Word2vec, the most popular word embedding model in recent years, serve as the foundation for various natural language processing (NLP) applications, such as biomedical information retrieval, relation extraction, and recommendation systems. The objective of this study is to examine how changes in the ratio of the biomedical domain to general domain data in the corpus affect the extraction of similar biomedical terms using Word2vec. We downloaded abstracts of 214,892 articles from PubMed Central (PMC) and the 3.9 GB Billion Word (BW) benchmark corpus from the computer science community. The datasets were preprocessed and grouped into 11 corpora based on the ratio of BW to PMC, ranging from 0:10 to 10:0, and then Word2vec models were trained on these corpora. The cosine similarities between the biomedical terms obtained from the Word2vec models were then compared in each model. The results indicated that the models trained with both BW and PMC data outperformed the model trained only with medical data. The similarity between the biomedical terms extracted by the Word2vec model increased when the ratio of the biomedical domain to general domain data was 3:7 to 5:5. This study allows NLP researchers to apply Word2vec based on more information and increase the similarity of extracted biomedical terms to improve their effectiveness in NLP applications, such as biomedical information extraction.

Background/Objectives: Widely known for its therapeutic benefits, hydrotherapy utilizes water’s physical properties, such as temperature, hydrostatic pressure, and viscosity, to influence physiological responses. Among these, body temperature modulation plays a crucial role in enhancing circulatory function, muscle relaxation, and metabolic processes. While hydrotherapy can improve systemic health, particularly cardiac function, improper temperature control poses risks, especially for vulnerable populations such as the elderly or individuals with thermoregulatory impairments. Therefore, accurately predicting post-bath body temperature is essential for ensuring safety and optimizing therapeutic outcomes. Methods: This study explored the use of fuzzy inference systems to predict post-bath body temperature, leveraging an adaptive neuro-fuzzy inference system, evolutionary fuzzy inference system (EVOFIS), and enhanced Takagi-Sugeno fuzzy system. These models were compared with random forest and support vector machine models using hydrotherapy-related data. Results: The results show that EVOFIS outperformed other models, particularly in predicting deep body temperature, which is clinically significant as it is closely linked to core physiological regulation. Conclusions: The ability to accurately forecast deep-temperature dynamics enables proactive management of hyperthermia risk, supporting safer hydrotherapy practices for at-risk groups. These findings highlight the potential of FIS-based models for non-invasive temperature prediction, contributing to enhanced safety and personalization in hydrotherapy applications.

[This corrects the article DOI: 10.2196/37881.].

BackgroundSocial media analysis tools have been used to monitor public sentiment and communication methods during public health emergencies.Public health emergencies are required to better understand the impact of the crisis on the public and to provide reference material for the prevention of future public health emergencies. We are concentrating on the sentiments around the public health emergency created by COVID-19.ObjectiveThis study aims to better understand the impact of public health emergencies on citizens and provide reference material for future public health emergency prevention.MethodsThe Fuzzy-c-means method was used to divide the 850,083 content of Weibo from January 24, 2020, to March 31, 2020, into seven categories of emotions: fear, happiness, disgust, surprise, sadness, anger, and good. The changes in emotion were tracked over time.ResultsThe results indicated that people showed "surprise" overall (55.89%); however with time, the "surprise" decreased. As the knowledge regarding the coronavirus disease 2019 (COVID-19) increased (contents about COVID-19 knowledge: from 21.16% to 4.19%), the "surprise" of the citizens decreased (from 59.95% to 46.58%). Citizens' feelings of "fear" and "good" increased as the number of deaths associated with COVID-19 increased ("fear”: from 15.42% to 20.95% "good”: 10.31% to 18.89%). As the infection was suppressed, the feelings of "fear" and "good" diminished ("fear”: from 20.95% to 15.79% "good”: from 18.89% to 8.46%).ConclusionsIn this study, the emotions and changes in emotions of Weibo users were analyzed in chronological order. The results of this study can prepare for future public health emergencies.

UNSTRUCTURED Correction: [Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users] In “[Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users” (JMIR Infodemiology Res 2024;4:e37881), the authors pointed out errors in the author's degree information. The author's information is presented as follows: Ryuichiro Ueda, MA; Feng Han, MA; Hongjian Zhang, MD; Tomohiro Aoki, MA; Katsuhiko Ogasawara, Prof Dr Has been changed to read as follows: Ryuichiro Ueda, MHA; Feng Han, MHA; Hongjian Zhang, PhD; Tomohiro Aoki, MHA; Katsuhiko Ogasawara, MBA, PhD. The correction will appear in the online version of the paper on the JMIR Publications website on February 28, 2024, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories. Ryuichiro Ueda, MA; Feng Han, MA; Hongjian Zhang, MD; Tomohiro Aoki, MA; Katsuhiko Ogasawara, Prof Dr Corrected: Ryuichiro Ueda, MHA; Feng Han, MHA; Hongjian Zhang, PhD; Tomohiro Aoki, MHA; Katsuhiko Ogasawara, MBA, PhD.