Analysis of emotional tendencies and discourse patterns in VKontakte social comments based on Nvivo12 encoding

Abstract

To study the emotional changes of the public during the COVID-19 epidemic, the experiment conducted an analysis of emotional tendencies and discourse patterns for comments on the VKontakte platform. The study first used Nvivo12 to classify comments on social platforms into topics, emotions, and relationship nodes. Then, a bidirectional long short-term memory network was introduced to comprehensively understand the context and classify positive and negative emotions. In addition, natural language processing toolkits were used to analyze the discourse structure of social comments, and support vector machines were used to discriminate the emotional tendencies of comments. According to the experimental analysis, during the period of rapid incidence rate increased, 27.6 % of the public exhibited positive emotional tendencies, while <39.3 % exhibited negative emotional tendencies. In the following three stages, the proportion of negative emotions in the public was greater than that of positive emotions. In the fourth stage of the epidemic, comments mainly concerned the supply of medical drugs, masks, and the construction and opening of hospitals. The existing problems indicate that the epidemic has had a significant impact on public emotions, and effective measures need to be taken to alleviate the negative emotions of the public. The research results are helpful in revealing the dynamic changes of public emotions and their discourse patterns during the COVID-19 epidemic, and provide a new perspective for understanding public emotions.