Journal of Guangdong University of Technology ›› 2022, Vol. 39 ›› Issue (03): 32-40,48.doi: 10.12052/gdutxb.210202

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A Research on Online Public Opinion Evolution of Public Emergencies Based on Stance Detection and Topic Mining

Liu Gao-yong, Huang Jing-zhao, Ai Dan-xiang   

  1. School of Management, Guangdong University of Technology, Guangzhou 510520, China
  • Received:2021-12-22 Online:2022-05-10 Published:2022-05-19

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Abstract: The research on the evolution of public opinions in public emergencies based on stance detection and topic mining can help the government and stakeholders quickly grasp the evolution law of online public opinions in public emergencies, which is of great significance. The public opinion life cycle of specific public emergencies is divided, and a new stance detection model and a topic sentence mining method are proposed. For each life cycle stage, on the basis of identifying the stance information of mass Internet users, the efficient public opinion information is screened out, and then the topics of efficient public opinion information are mined, so as to deeply analyze the evolution law of the topic information of public emergencies. Taking the microblog data of "A Hangzhou Woman Missing Case" as an example, firstly, the experimental results of this method and various methods are compared to verify the effectiveness of this method.Then, the evolution of public opinions is analyzed based on the experimental results of this method, which proves that this method can quickly focus on the key points in the actual public opinions of public emergencies, and better analyze the evolution law and characteristics of public opinions of public emergencies. This method can effectively and accurately identify and analyze the content of public opinions, and provide a new perspective for the study of the evolution of network public opinions.

Key words: stance detection, topic mining, public emergency, evolution of public opinion

CLC Number: 

  • C916
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