Analysis of Online Public Opinion Texts Based on Topic Mining and Sentiment Analysis

Text-based public opinion analysis is an important branch of natural language processing. It aims to collect, analyze, and interpret the opinions, emotions, and attitudes of the general public to understand and predict their reactions to specific events, policies, or companies. In recent years, this field has become a research hotspot. This paper reviews methods of public opinion analysis based on topic mining and sentiment analysis, exploring their concepts and characteristics and analyzing recent research achievements. By comparing the advantages and disadvantages of different methods, this paper summarizes the strengths and limitations of these approaches. Based on a review of the current state of research both domestically and internationally, this paper provides an in-depth analysis of public opinion analysis methods and proposes future directions and trends for development.