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Changes in Public Sentiment under the Background of Major Emergencies—Taking the Shanghai Epidemic as an Example

Author

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  • Bowen Zhang

    (School of Earth Sciences, Yunnan University, Kunming 650000, China
    These authors contributed equally to this work.)

  • Jinping Lin

    (School of Earth Sciences, Yunnan University, Kunming 650000, China
    These authors contributed equally to this work.)

  • Man Luo

    (School of Earth Sciences, Yunnan University, Kunming 650000, China)

  • Changxian Zeng

    (Faculty of Science, Dalian University for Nationalities, Dalian 116000, China)

  • Jiajia Feng

    (School of Earth Sciences, Yunnan University, Kunming 650000, China)

  • Meiqi Zhou

    (School of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650000, China)

  • Fuying Deng

    (School of Earth Sciences, Yunnan University, Kunming 650000, China)

Abstract

The occurrence of major health events can have a significant impact on public mood and mental health. In this study, we selected Shanghai during the 2019 novel coronavirus pandemic as a case study and Weibo texts as the data source. The ERNIE pre-training model was used to classify the text data into five emotional categories: gratitude, confidence, sadness, anger, and no emotion. The changes in public sentiment and potential influencing factors were analyzed with the emotional sequence diagram method. We also examined the causal relationship between the epidemic and public sentiment, as well as positive and negative emotions. The study found: (1) public sentiment during the epidemic was primarily affected by public behavior, government behavior, and the severity of the epidemic. (2) From the perspective of time series changes, the changes in public emotions during the epidemic were divided into emotional fermentation, emotional climax, and emotional chaos periods. (3) There was a clear causal relationship between the epidemic and the changes in public emotions, and the impact on negative emotions was greater than that of positive emotions. Additionally, positive emotions had a certain inhibitory effect on negative emotions.

Suggested Citation

  • Bowen Zhang & Jinping Lin & Man Luo & Changxian Zeng & Jiajia Feng & Meiqi Zhou & Fuying Deng, 2022. "Changes in Public Sentiment under the Background of Major Emergencies—Taking the Shanghai Epidemic as an Example," IJERPH, MDPI, vol. 19(19), pages 1-20, October.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12594-:d:931955
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    References listed on IDEAS

    as
    1. Kwon, Okyu & Yang, Jae-Suk, 2008. "Information flow between composite stock index and individual stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2851-2856.
    2. Hind Bitar & Amal Babour & Fatema Nafa & Ohoud Alzamzami & Sarah Alismail, 2022. "Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study," IJERPH, MDPI, vol. 19(13), pages 1-15, July.
    3. Okyu Kwon & Jae-Suk Yang, 2008. "Information flow between stock indices," Papers 0802.1747, arXiv.org.
    4. Kim, Yup & Kim, Jinho & Yook, Soon-Hyung, 2015. "Information transfer network of global market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 39-45.
    5. Qiang Bao & Xujuan Zhang & Xijuan Wu & Qiang Zhang & Jinshou Chen, 2021. "Research on Public Environmental Perception of Emotion, Taking Haze as an Example," IJERPH, MDPI, vol. 18(22), pages 1-16, November.
    6. Nag, Sayan & Basu, Medha & Sanyal, Shankha & Banerjee, Archi & Ghosh, Dipak, 2022. "On the application of deep learning and multifractal techniques to classify emotions and instruments using Indian Classical Music," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    7. Yuewei Wang & Mengmeng Xi & Hang Chen & Cong Lu, 2022. "Evolution and Driving Mechanism of Tourism Flow Networks in the Yangtze River Delta Urban Agglomeration Based on Social Network Analysis and Geographic Information System: A Double-Network Perspective," Sustainability, MDPI, vol. 14(13), pages 1-21, June.
    8. Bekiros, Stelios & Nguyen, Duc Khuong & Sandoval Junior, Leonidas & Uddin, Gazi Salah, 2017. "Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets," European Journal of Operational Research, Elsevier, vol. 256(3), pages 945-961.
    9. Ali Feizollah & Nor Badrul Anuar & Riyadh Mehdi & Ahmad Firdaus & Ainin Sulaiman, 2022. "Understanding COVID-19 Halal Vaccination Discourse on Facebook and Twitter Using Aspect-Based Sentiment Analysis and Text Emotion Analysis," IJERPH, MDPI, vol. 19(10), pages 1-17, May.
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