Public sentiment dynamics in policy transitions: a sentiment analysis based on Weibo data
Xuan Ning, Ruonan Li, Dewei Lan, Chaofan Chen, Yupeng Li

TL;DR
This study analyzes public sentiment changes on Weibo in response to China's shift from strict to open pandemic policies in late 2022.
Contribution
The study provides new insights into public emotional responses to abrupt policy changes using Weibo data and LDA topic modeling.
Findings
Disgust became the most prevalent emotion after mid-December, surpassing 'like' in public sentiment.
Happiness gradually increased to 12.0% during the study period.
Topic modeling revealed cultural and social factors influencing emotional fluctuations.
Abstract
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…
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Taxonomy
TopicsData-Driven Disease Surveillance · Sentiment Analysis and Opinion Mining · Computational and Text Analysis Methods
