Analysis of Public Emotion and Disseminator-Audience Framework during Outbreak of Public Crisis Events: A case study of Microblog Account Xiake Island

This study crawled the cross-sectional data of the contents and comments from Microblog Account Xiake Island during the outbreak of coronavirus pneumonia as subjects, to examine the deviation and resonance association among affective fluctuations of the Chinese public, media framework, and audiences' cognitive framework. Using SnowNLP to conduct sentiment analysis of text comments, we found that during the outbreak of coronavirus pneumonia, the public spent most of the time in low-intensity negative affectivity, and the average affective propensity in response to individual microblog fluctuated greatly, and the public was easily caught in an emotional frenzy, which reduces the level of trust in government. Through a comparison of public affectivity and related epidemic data, Xiake Island focuses on reporting emotional facts, whose construction of social reality contains obvious emotional trajectories. Clustering analysis of thematic framework by LDA algorithm reveals that in terms of framework, the framework Xiake Island uses resonates to a large degree with the framework users focus on. In terms of the level of concerns over the framework, Xiake Island deviates to a certain extent from the public. This deviation, together with the strategy of focusing on reporting emotional facts, is a discursive strategy adopted by the new mainstream media to seek the reconstruction of cultural leadership. © 2022 Owner/Author.

Research on online emotion of COVID-19 based on text sentiment analysis

The growth of internet users and the convenience of internet communication provide a foundation for the formation of internet emotions. As the internet and real-life interactions become closer, the influence of internet emotions on society is increasing. Therefore, taking the spread of COVID-19 in Xinjiang in 2020 as an example, 43,111 related micro-blog texts were collected. After a series of operations such as Chinese word segmentation, POS tagging, data cleaning, text representation, feature extraction and so on, thematic extraction and text sentiment analysis were carried out to get people's comment themes, emotional tendencies and COVID-19's network emotional situation. The results show that the public will have a better understanding of the cause of COVID-19 disease and its infectiousness, preventive measures and cure as time goes on. The research of this paper can help the relevant government departments to perceive and guide the network emotional situation.