Enhanced sentiment analysis regarding COVID-19 news from global channels

Waseem Ahmad (), Bang Wang (), Philecia Martin (), Minghua Xu () and Han Xu ()
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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

Journal of Computational Social Science, 2023, vol. 6, issue 1, No 2, 19-57

Abstract: 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.

Keywords: Sentiment analysis; News media; Deep learning; COVID-19; Vaccine (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s42001-022-00189-1

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