At present,China is in a rapid transition period where social contradictions are increasingly prominent.Online public opinion triggered by various emergencies has become an important factor affecting social stability.In-depth analysis of the distribution and multi-dimensional evolution of hot search topics re-lated to emergencies on the Internet is of great significance for public opinion guidance and governance.By using Python to crawl Weibo hot search data from January 1,2023,to December 31,2023—including data fields such as text,category,popularity—and after filtering through text analysis methods like Chinese senti-ment classification models,we ultimately obtained 14,841 valid data entries.We processed the Weibo hot search texts using tools like HanLP for word segmentation and AipNLP for sentiment calculation.Using the Tomotopy tool,we constructed an LDA topic model to infer the thematic distribution of online public opinion in emergencies.Subsequently,combining methods like text mining,sentiment calculation,and semantic a-nalysis,we conducted systematic mining and visual analysis of the evolution process of online public opinion in emergencies from three aspects:popularity,sentiment,and topics.The study found that in 2023,China's online public opinion in emergencies mainly revolves around four major themes:natural disasters,social security,personal safety,and international conflicts.The generation,catalysis,and diffusion of online pub-lic opinion in emergencies have important implications for public cognition,government governance,and platform supervision.
Weibo Hot Search DataEmergenciesOnline Public OpinionGovernment GovernanceSemantic Network Analysis
NETL
NSTL
万方数据
