Analysis of COVID-19-Related User Content on the Baseball Bulletin Board in 2020 through Text Mining

The world engaged in online sport watching during COVID-19. Fortunately, Taiwan, the pandemic was stably controlled 2020, allowing for continuation of Chinese Professional Baseball League (CPBL); this attracted international attention and encouraged relevant discussions on social media Taiwan. In present study, through text mining, we analyzed user content (e.g., concepts sports service quality identity) Technology Temple (PTT) baseball board—the largest bulletin board system A predictive model constructed to assess PTT users’ COVID-19-related comments 2020. total 422 articles 21,167 were retrieved. users interacted more frequently closed-door period, particularly beginning CPBL April. Effective prevention, which garnered global league, generated a sense national identity among users, strengthened with development peripheral products, such as English broadcasting live Twitch. We used machine learning develop chatbot predicting attributes comments; may improve teams’ understanding public opinion trends. Our findings help stakeholders tailored programs spectators situations.