Text recognition and analysis of network public opinion focus events of a major epidemic: a case study of “COVID-19” in Sina Microblogs

Abstract

Identifying and analyzing the public’s opinion of focal events during a major epidemic can help the government grasp the vicissitudes of network public opinion in a timely manner and provide the appropriate responses. Taking the COVID-19 epidemic as an example, this study begins by using Python-selenium to capture the original text and comment data related to COVID-19 from Sina Microblog’s CCTV News from Jan. 19, 2020, to Feb. 20, 2020. The study subsequently uses a manual interpretation method to classify the Weibo content and analyzes the shifting focus phenomena of network public opinion based on the moving average method. Next, the study uses an enhances TF-IDF to extract keywords from the Weibo comment and uses the keywords to construct a word co-occurrence network. The results show that during the epidemic, the network public opinion focus shifted significantly over time. With the progression of the epidemic, the focus of network public opinion diversified, and various categories stabilized. Compared to simple keyword and text classification recognition focus problems, the proposed model, which is highly accurate, identified multiple network public opinion focus problems and described the core contradictions of the different focus problems.