Abstract
To explore the emotional attitudes of microblog users in the different COVID-19 stages in China, this study used data mining and machine-learning methods to crawl 112,537 Sina COVID-19- related microblogs and conduct sentiment and group difference analyses. It was found that: (1) the microblog users' emotions shifted from negative to positive from the second COVID-19 pandemic phase; (2) there were no significant differences in the microblog users' emotions in the different regions; (3) males were more optimistic than females in the early stages of the pandemic; however, females were more optimistic than males in the last three stages; and (4) females posted more microblogs and expressed more sadness and fear while males expressed more anger and disgust. This research captured online information in real-time, with the results providing a reference for future research into public opinion and emotional reactions to crises.
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