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The Evolution of Public Opinion and Its Emotion Analysis in Public Health Emergency Based on Weibo Data

In: Liss 2022

Author

Listed:
  • Jiazheng Sun

    (University of Science and Technology Beijing)

  • Xiaodong Zhang

    (University of Science and Technology Beijing)

  • Shaojuan Lei

    (University of Science and Technology Beijing)

Abstract

Since the occurrence of the Corona Virus Disease 2019, relevant online public opinion has spread rapidly, which has had an important impact on social order. How to identify, prevent and control public opinion crisis of public health emergencies has become a practical problem that urgently needs to be studied. First data source comes from Weibo comments, comparing with the three models of Naive Bayesian Model, Support Vector Machine and Logistic Regression, Long Short Term Memory (LSTM) model based on word to vector (Word2vec) model is selected for emotion classification. Secondly, the evolution of public opinion is divided into three stages base to the Baidu search index, use visualization methods to study emotional tendencies at various stages and analyze the temporal and spatial laws of public opinion. At last, according to the evolution law and characteristics of public opinion in each stage, relevant optimization strategies are proposed. Research shows, the Word2Vec-LSTM model can effectively predict the emotional state of netizens; analyzes the law of public opinion evolution of public health emergencies, provide a basis for optimizing the network environment and preventing public opinion crisis.

Suggested Citation

  • Jiazheng Sun & Xiaodong Zhang & Shaojuan Lei, 2023. "The Evolution of Public Opinion and Its Emotion Analysis in Public Health Emergency Based on Weibo Data," Lecture Notes in Operations Research, in: Xiaopu Shang & Xiaowen Fu & Yixuan Ma & Daqing Gong & Juliang Zhang (ed.), Liss 2022, pages 415-434, Springer.
    • Handle: RePEc:spr:lnopch:978-981-99-2625-1_33
    DOI: 10.1007/978-981-99-2625-1_33
    as

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