Strategies for Monitoring and Managing Online Public Opinion in Universities Under the Background of Big Data

Abstract

In response to the issue of some keywords not being logged in or having inaccurate semantics in current public opinion monitoring, this article uses an improved word segmentation method to extract semantic features. In response to the current issue of unable to control the emotional direction of public opinion comments in public opinion analysis, an emotion analysis model Bi_GRU is proposed for sentiment analysis. Finally, using students’ commonly used Weibo as a verification scenario, sensitive information such as “food safety” and “campus bullying” is screened to control the emotional direction of college students. The final proof is that the method proposed in this article can effectively supervise public opinion in a centralized environment and provide effective means for student management.