ArticleIEEE access : practical innovations, open solutions2021
Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Current applications and future directions in natural language processing for news media and mental health.Scientific reports · 2025Article
- Surveillance of Gun-Related Conversations on Twitter.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media.Journal of medical Internet research · 2024Article
- Beyond fear and anger: A global analysis of emotional response to Covid-19 news on Twitter using deep learning.Online social networks and media · 2023Article
- Enhanced sentiment analysis regarding COVID-19 news from global channels.Journal of computational social science · 2023Article
- A two-staged NLP-based framework for assessing the sentiments on Indian supreme court judgments.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2023Article
- CovTiNet: Covid text identification network using attention-based positional embedding feature fusion.Neural computing & applications · 2023Article
- Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing.International journal of data science and analytics · 2023Article
- Article
- Framing of COVID-19 in Newspapers: A Perspective from the US-Mexico Border.Healthcare (Basel, Switzerland) · 2022Article
- Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration.Biotech (Basel (Switzerland)) · 2022Article
- Article
- A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts.Frontiers in sociology · 2022Article
- A Novel Approach of Feature Space Reconstruction with Three-Way Decisions for Long-Tailed Text Classification.Computational intelligence and neuroscience · 2022Article
- Article
- Real-Time Infoveillance of Moroccan Social Media Users' Sentiments towards the COVID-19 Pandemic and Its Management.International journal of environmental research and public health · 2021Article
- Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach.IEEE access : practical innovations, open solutions · 2021Article
- Text classification technique for discovering country-based publications from international COVID-19 publications.Digital healthArticle
- Hate Speech in a Telegram Conspiracy Channel During the First Year of the COVID-19 Pandemic.Social media + societyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.