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Enhanced sentiment analysis regarding COVID-19 news from global channels

Author

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  • Waseem Ahmad

    (Huazhong University of Science and Technology)

  • Bang Wang

    (Huazhong University of Science and Technology)

  • Philecia Martin

    (Huazhong University of Science and Technology)

  • Minghua Xu

    (Huazhong University of Science and Technology)

  • Han Xu

    (Huazhong University of Science and Technology)

Abstract

For a healthy society to exist, it is crucial for the media to focus on disease-related issues so that more people are widely aware of them and reduce health risks. Recently, deep neural networks have become a popular tool for textual sentiment analysis, which can provide valuable insights and real-time monitoring and analysis regarding health issues. In this paper, as part of an effort to develop an effective model that can elicit public sentiment on COVID-19 news, we propose a novel approach Cov-Att-BiLSTM for sentiment analysis of COVID-19 news headlines using deep neural networks. We integrate attention mechanisms, embedding techniques, and semantic level data labeling into the prediction process to enhance the accuracy. To evaluate the proposed approach, we compared it to several deep and machine learning classifiers using various metrics of categorization efficiency and prediction quality, and the experimental results demonstrate its superiority with 0.931 testing accuracy. Furthermore, 73,138 pandemic-related tweets posted on six global channels were analyzed by the proposed approach, which accurately reflects global coverage of COVID-19 news and vaccination.

Suggested Citation

  • Waseem Ahmad & Bang Wang & Philecia Martin & Minghua Xu & Han Xu, 2023. "Enhanced sentiment analysis regarding COVID-19 news from global channels," Journal of Computational Social Science, Springer, vol. 6(1), pages 19-57, April.
    • Handle: RePEc:spr:jcsosc:v:6:y:2023:i:1:d:10.1007_s42001-022-00189-1
    DOI: 10.1007/s42001-022-00189-1
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    References listed on IDEAS

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    1. Amartya Chakraborty & Sunanda Bose, 2020. "Around the world in 60 days: an exploratory study of impact of COVID-19 on online global news sentiment," Journal of Computational Social Science, Springer, vol. 3(2), pages 367-400, November.
    2. Harleen Kaur & Shafqat Ul Ahsaan & Bhavya Alankar & Victor Chang, 2021. "A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets," Information Systems Frontiers, Springer, vol. 23(6), pages 1417-1429, December.
    3. Nicholas Francis Havey, 2020. "Partisan public health: how does political ideology influence support for COVID-19 related misinformation?," Journal of Computational Social Science, Springer, vol. 3(2), pages 319-342, November.
    4. Gavin Abercrombie & Riza Batista-Navarro, 2020. "Sentiment and position-taking analysis of parliamentary debates: a systematic literature review," Journal of Computational Social Science, Springer, vol. 3(1), pages 245-270, April.
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    Cited by:

    1. Carlos Henríquez Miranda & German Sanchez-Torres & Dixon Salcedo, 2023. "Exploring the Evolution of Sentiment in Spanish Pandemic Tweets: A Data Analysis Based on a Fine-Tuned BERT Architecture," Data, MDPI, vol. 8(6), pages 1-18, May.

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