In this study, we carried out a text analysis on the information disseminated and discussed among netizens on the Baidu Post Bar (the world’s largest Chinese forum) during the coronavirus disease 2019 (COVID-19) epidemic, to create a policy basis for health administrative departments.

We used Python tools to search for the relevant data on the Baidu Post Bar. Next, a text analysis was performed on the posts’ contents using a combination of latent Dirichlet allocation (LDA), sentiment analysis, and correlation analysis.

According to the LDA analysis, the public was highly interested in topics such as COVID-19 prevention, infection symptoms, infection and coping measures, sources of transmission and treatments, community management, and work resumption. The majority of the public had negative emotional values, yet a portion of the public held positive emotional values. We also performed a correlation analysis of the influencing factors was established.

Netizens’ degree of concern shown in their posts was greatly associated with the spread of COVID-19. With the rise, diffusion, outbreak, and mitigation of COVID-19 in China, netizens have successively created a large number of posts, and the topics of discussion varied over time. Therefore, the media and the government have the responsibility to distribute positive information, to correctly guide the public’s emotions to bring some sort of reassurance to the public.