Sentiment analysis and prediction model based on Chinese government affairs microblogs

Abstract

Existing sentiment analysis research on Chinese government affairs microblogs primarily focuses on the task of sentiment classification on microblogs. There has been a lack of investigation into the correlation of each government affairs microblog with the sentiment values of the corresponding comments below it. This study constructs a large-scale government affairs microblog dataset and explore the correlation of each microblog with the sentiment values of the corresponding comments below it. We proposed a new framework that includes data collection, sentiment analysis and sentiment prediction model training. This sentiment analysis framework is crucial in the government's understanding of the public's real-time sentiments toward policies. It also helps monitor the Internet public sentiment and actively guide the Internet public opinion. We first analyzed the sentiment distribution of government affairs microblogs and the sentiment values on meaningful words. We also discussed the discrepancy in text similarity and sentiment values between microblogs. Furthermore, we investigated the extreme emotional content and discussed the factors influencing the sentiment values of comments. Finally, we designed a collaborative attention regression model to predict the sentiments of microblogs. The sentiment prediction model performed well in the sentiment prediction regression task. The sentiment analysis and the prediction framework for government affairs microblogs in this study can be used as a reference for government-related Internet opinion monitoring.