@article{TEXTUAL,
      recid = {11603},
      author = {Jiang, Duoji},
      title = {Aftershock: Sentiment and Content Analysis of Weibo Posts  about 2023 Health Insurance Reform in the Post Zero-Covid  Context},
      journal = {Lecture Notes in Education Psychology and Public Media},
      address = {2024-01-03},
      number = {TEXTUAL},
      abstract = {This study investigates the public sentiment and thematic  content of Weibo posts regarding the 2023 health insurance  reform in China, a topic of heightened discussion in the  aftermath of the country's zero-Covid policy. Utilizing a  data-driven approach, the research analyzes netizens'  reactions using phrase-level sentiment analysis and topic  analysis techniques. The paper identifies the financial  strains imposed by zero-Covid as a primary driver behind  the reform, which led to reduced benefits and increased  dissatisfaction among the population. Methodologically, the  study employs Support Vector Regressor for sentiment  analysis and Word2Vec for content analysis, with data  sourced from an open-source Weibo crawler. The findings  reveal a discrepancy in sentiment between official and  unofficial accounts, pinpoint the sources of negative  sentiment, and highlight the interconnection with the  zero-Covid policy. The research provides insights into the  public's perception of government policies during a  transitional period in China's health insurance landscape.},
      url = {http://knowledge.uchicago.edu/record/11603},
}