The analysis of collective social sentiment using large-scale data obtained from the Internet, such as social media data, has been actively conducted in recent years, but not many of them considered geographical distributions of sentiments or their spatial dynamics. In this study, we analyzed tweets associated with location information to detect local collective sentiment of each prefecture in Japan, especially in response to societal events. To extract positive and negative sentiments, we used emojis as language-independent universal indicators of positive/negative sentiments. We found that negative sentiment increased nationwide on the day of a major typhoon hit and after the onset of a COVID-19 pandemic in Japan, while positive sentiment increased around Christmas and the announcement of university or high school admission decisions, with some geographical variations. Then, we computed the correlation coefficient of the number of positive tweets on the same day and observed the relationship between the prefectures. We also built a linear regression model to forecast the local positive sentiment of a prefecture from other prefectures' past values, which achieved a reasonable predictability with R2 = 0.5-0.6. Based on the coefficient matrix of this sentiment forecast model, we constructed a causal network of prefecture sentiments in Japan. Interestingly, the relationships among prefectures and their centralities changed significantly before and after the COVID-19 pandemic.

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