Sentiment Analysis of Japanese Twitter Users: Verification in the Early Stages of COVID-19 Infection Spread.

BACKGROUND: The COVID-19 outbreak province prompted global behavioral restrictions, impacting public mental health. Sentiment analysis, a tool 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 declaration in Japan. By analyzing both user-generated tweets and retweets, the study aims to discern patterns in emotional responses during this critical period.

METHODS: We conducted a day-by-day analysis of Twitter data using 4,894,009 tweets containing the keywords "corona," "COVID-19," and "new pneumonia" from March 23 to April 21, 2020, approximately two weeks before and after the first declaration of a state of emergency in Japan. We also processed tweet data into vectors for each word, employed the Fuzzy-C-Means (FCM) method, a type of cluster analysis, for the words in the sentiment dictionary, setting up seven sentiment clusters (negative: anger, sadness, surprise, disgust; neutral: anxiety; positive: trust and joy), and conducted sentiment analysis divided into 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 exhibited a tendency to share more negative content compared to the tweet group.

CONCLUSIONS: This study employed sentiment analysis on Japanese tweets and retweets (RTs) to explore public sentiments during the early stages of COVID-19 in Japan, spanning two weeks before and after the first state of emergency declaration. The analysis revealed a mix of positive (joy) and negative (anxiety, disgust) feelings. Significantly, joy increased in the RT group post-emergency declaration, but this group also exhibited a tendency to share more negative content than the Tweet group. The 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.