The evolution of public sentiment toward government management of emergencies: Social media analytics

Abstract

At present, social media have become the main media of network public opinion (PO) dissemination. By analyzing the trend of emotional development in public emergencies, we can explore the evolution law of PO and identify potential risks, which provide decision support for the guidance and control of government management. First, based on the concept of critical points in the complex system, this study established a public sentiment (PS) evolution model under public emergencies and proposed an algorithm to identify the critical points in PS based on microblog data analysis. In addition, the BC-BIRCH algorithm was used to construct a topic clustering model for public emergencies, which improved the effect of topic discovery by merging multiple topic clusters. The evolution of public emergencies was analyzed by calculating the emotional heat value of different topic events. Finally, experimental results showed that the emotion of netizens' fluctuates greatly in the initial stage of PO under different themes. The method used in this paper achieved good results in topic clustering, critical point prediction, and PO evolution analysis of public emergencies. The main contribution of this paper is to analyze the evolution of the internal mechanism of PS and to identify and predict key nodes such as the outbreak and extinction of netizens' sentiment based on data-driven methods so as to provide the basis and support to the government and related media as the main body of prevention and control to respond in advance and guide in time.

An Analysis of the Evolution of Public Sentiment and Spatio-Temporal Dynamics Regarding Building Collapse Accidents Based on Sina Weibo Data

“…The roots of sentiment analysis can be traced back to early 20th-century studies on public opinion analysis and the subjective analysis of texts conducted by the computational linguistics community in the 1990s. With the proliferation of social media platforms such as Twitter and Facebook, research in this field grew 50-fold between 2005 and 2016 [18]. There are two primary methods used for text sentiment analysis.…”