Investigating COVID-19 News across Four Nations: A Topic Modeling and Sentiment Analysis Approach

Piyush Ghasiya, Koji Okamura

研究成果: ジャーナルへの寄稿学術誌査読

81 被引用数 (Scopus)

抄録

Newspapers are very important for a society as they inform citizens about the events around them and how they can impact their life. Their importance becomes more crucial and indispensable in the times of health crisis such as the current COVID-19 pandemic. Since the starting of this pandemic newspapers are providing rich information to the public about various issues such as the discovery of a new strain of coronavirus, lockdown and other restrictions, government policies, and information related to the vaccine development for the same. In this scenario, analysis of emergent and widely reported topics/themes/issues and associated sentiments from various countries can help us better understand the COVID-19 pandemic. In our research, the database of more than 100,000 COVID-19 news headlines and articles were analyzed using top2vec (for topic modeling) and RoBERTa (for sentiment classification and analysis). Our topic modeling results highlighted that education, economy, US, and sports are some of the most common and widely reported themes across UK, India, Japan, South Korea. Further, our sentiment classification model achieved 90% validation accuracy and the analysis showed that the worst affected country, i.e. the UK (in our dataset) also has the highest percentage of negative sentiment.

本文言語英語
論文番号9366469
ページ(範囲)36645-36656
ページ数12
ジャーナルIEEE Access
9
DOI
出版ステータス出版済み - 2021

!!!All Science Journal Classification (ASJC) codes

  • 工学一般
  • コンピュータサイエンス一般
  • 電子工学および電気工学
  • 材料科学一般

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