Enhanced sentiment analysis regarding COVID-19 news from global channels.

IF 2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Journal of Computational Social Science Pub Date : 2023-01-01 Epub Date: 2022-11-27 DOI:10.1007/s42001-022-00189-1
Waseem Ahmad, Bang Wang, Philecia Martin, Minghua Xu, Han Xu
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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.

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加强对来自全球渠道的 COVID-19 新闻的情感分析。
为了实现健康社会,媒体必须关注疾病相关问题,让更多人广泛了解这些问题,降低健康风险。最近,深度神经网络已成为文本情感分析的一种流行工具,它可以为健康问题提供有价值的见解和实时监测与分析。在本文中,为了开发一种有效的模型来激发公众对 COVID-19 新闻的情感,我们提出了一种利用深度神经网络对 COVID-19 新闻标题进行情感分析的新方法 Cov-Att-BiLSTM。我们在预测过程中整合了注意力机制、嵌入技术和语义级数据标签,以提高预测的准确性。为了评估所提出的方法,我们使用分类效率和预测质量的各种指标将其与几种深度学习和机器学习分类器进行了比较,实验结果表明其优越性,测试准确率为 0.931。此外,该方法还分析了全球六个频道发布的 73 138 条大流行病相关推文,准确反映了 COVID-19 新闻和疫苗接种的全球覆盖情况。
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来源期刊
Journal of Computational Social Science
Journal of Computational Social Science SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
6.20
自引率
6.20%
发文量
30
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