基于微博大数据的高考舆情研究：热度与情感成分的时空变化及宏观因素影响分析

Based on the textual content of Weibo big data, this study analyzes the changes in the heat and sentiment components of public opinion about the college entrance examination in various provincial-level administrative regions in mainland China between 2019 and 2024, and explores the relationship between these changes and macroeconomic indicators. The study used the number of text entries as the heat index and analyzed it with Baidu Index, and the results showed that the heat of public opinion related to the college entrance examination varied across years and provinces, and was closely related to local economic indicators such as per capita gross regional product and local financial expenditure on education. And the sentiment component analysis with the help of the Linxian analysis system and the Dalian Polytechnic Sentiment Dictionary shows that the texts related to the college entrance examination show different component characteristics in different time periods and regions, and are associated with some realistic factors. This study provides a new big data perspective for understanding the changing patterns of the heat and sentiment components of public opinion on the college entrance examination, and its relationship with socioeconomic factors.