ORIGINAL RESEARCH article
Front. Public Health
Sec. Public Health Policy
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1692166
Public Sentiment Dynamics in Policy Transitions: A Sentiment Analysis Based on Weibo Data
Provisionally accepted
- 1Beijing Normal-Hong Kong Baptist University, Zhuhai, China
- 2The Chinese University of Hong Kong, Hong Kong, Hong Kong, SAR China
- 3Hong Kong Baptist University, Hong Kong, Hong Kong, SAR China
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China had been implementing stringent dynamic policies during the COVID-19 pandemic. In late 2022, China made a sudden policy shift from its three-year dynamic zero-COVID to the re-opening policy, which resulted in a divergence of online public opinions and varying sentiments. However, few research has been done to explore the public's sentiment changes toward this abrupt policy shift. To better inform effective health communication regarding governments' change of policies for future initiatives, this study aims to analyze public's sentiment changes toward the launching of China's re-opening policy by using Weibo data. Our study examined 1, 423, 694 Weibo posts during the period from November 11, 2022 to January 11, 2023 to conduct a fine-grained emotion extraction. This study also used the LDA topic model to extract potential topics in Weibo posts to align topics and corresponding emotions for generating in-depth understanding. Fluctuations of different emotions during these two months were profoundly analyzed and interpreted by taking cultural, social, and policy-related reasons into consideration. Notably, the average proportion of "disgust" (24.0%) exceeded that of "like" (22.8%) after mid-December, while "happiness" exhibited a gradual increase to 12.0%. Results of this study will be essential to informing the government's effective health communication in the time of public health crisis, facilitating pandemic control and prevention, and enlightening on the maintenance of public's well-being.
Keywords: COVID-19, Health Communication, LDA topic model, Public health policies, sentiment analysis, Weibo
Received: 25 Aug 2025; Accepted: 14 Oct 2025.
Copyright: © 2025 Ning, Li, Lan, Chen and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ruonan Li, s230032024@mail.uic.edu.cn
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