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Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19

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  • Meng Hsiu Tsai

    (Department of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA)

  • Yingfeng Wang

    (Department of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA)

Abstract

Policymakers and relevant public health authorities can analyze people’s attitudes towards public health policies and events using sentiment analysis. Sentiment analysis focuses on classifying and analyzing text sentiments. A Twitter sentiment analysis has the potential to monitor people’s attitudes towards public health policies and events. Here, we explore the feasibility of using Twitter data to build a surveillance system for monitoring people’s attitudes towards public health policies and events since the beginning of the COVID-19 pandemic. In this study, we conducted a sentiment analysis of Twitter data. We analyzed the relationship between the sentiment changes in COVID-19-related tweets and public health policies and events. Furthermore, to improve the performance of the early trained model, we developed a data preprocessing approach by using the pre-trained model and early Twitter data, which were available at the beginning of the pandemic. Our study identified a strong correlation between the sentiment changes in COVID-19-related Twitter data and public health policies and events. Additionally, the experimental results suggested that the data preprocessing approach improved the performance of the early trained model. This study verified the feasibility of developing a fast and low-human-effort surveillance system for monitoring people’s attitudes towards public health policies and events during a pandemic by analyzing Twitter data. Based on the pre-trained model and early Twitter data, we can quickly build a model for the surveillance system.

Suggested Citation

  • Meng Hsiu Tsai & Yingfeng Wang, 2021. "Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19," IJERPH, MDPI, vol. 18(12), pages 1-14, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6272-:d:572268
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    References listed on IDEAS

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    Cited by:

    1. Bharati Sanjay Ainapure & Reshma Nitin Pise & Prathiba Reddy & Bhargav Appasani & Avireni Srinivasulu & Mohammad S. Khan & Nicu Bizon, 2023. "Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    2. Zhihang Liu & Jinlin Wu & Connor Y. H. Wu & Xinming Xia, 2024. "Shifting sentiments: analyzing public reaction to COVID-19 containment policies in Wuhan and Shanghai through Weibo data," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.

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