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Article type: Research Article
Authors: Qiu, Donga; b; * | Huang, Lina
Affiliations: [a] College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China | [b] School of Mathematics, Southwest Jiaotong University, Xipu, Chengdu, Sichuan, China
Correspondence: [*] Corresponding author: Dong Qiu, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. E-mail: [email protected].
Abstract: Since the outbreak of COVID-19 (Corona Virus Disease 2019), the Chinese government has taken strict measures to prevent and control the epidemic. Although the spread of the virus has been controlled, people’s daily life and work have been affected and restricted to varying degrees. Thus people have different sentiments, these may affect people’s implementation and compliance with the policies, thus affecting the effectiveness of epidemic prevention and control. At present, few pieces of literature have analyzed the relationships between people’s feelings, policies, and epidemic trends. The object of this paper is to analyze the text content on social media, to find out the impact of the epidemic blockade policy on the public mood and the concerns expressed by the public about policies changes, and the interaction between policies and epidemic states at different stages of the epidemic. In this paper, we collected the posts of two cities where the epidemic occurred at the same time for analysis and comparative study. On the one hand, we revealed the changes in public attention and attitudes in the two regions during the epidemic, the other hand, it also reflects the differences in public sentiment between the two regions, as well as the correlation between emotions and policies and epidemic trends when different policies are adopted under different circumstances. The obtained results have a certain guiding significance for public health departments to formulate reasonable epidemic prevention policies.
Keywords: COVID-19, sentiment analysis, pidemic prevention policy
DOI: 10.3233/IDA-230025
Journal: Intelligent Data Analysis, vol. 28, no. 2, pp. 533-552, 2024
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