Abstract:  In order to analyze the evolution of public opinion under emergencies and discover the law of the evolution of public opinion, a sentiment feature-based public opinion evolution analysis method was proposed, includdinga News Sentiment Analysis Module and a Public Opinion Evolution Analysis Module. The News Sentiment Analysis Module was based on the BERT pre-training model and the BiGRU model, where BERT was extracted as a word embedding, and BiGRU was employed to extract the contextual links of the textual feature vector to achieve a better understanding of the sentiment polarity of public opinion data. In the Public Opinion Evolution Analysis Module, this paper modeled the dynamic visualization of the sentiment features of public opinion in the time dimension, and then based on the visualization results, enabled the resolution of evolutionary patterns of public opinion data. Finally, a numerical experiment was conducted using one million pieces of the COVID-19 news data from January 1, 2020 to February 19, 2020. The experimental results show that the method proposed in this paper can effectively analyze the sentiment polarity of public opinion data. 

Key words:  , COVID-19, analysis of public sentiment and emotion, analysis of public opinion evolution