Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Mar 1:9:36645-36656.
doi: 10.1109/ACCESS.2021.3062875. eCollection 2021.

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

Piyush Ghasiya  1 , Koji Okamura  2
Affiliations

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

Piyush Ghasiya et al. IEEE Access. .

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.

Keywords: COVID-19; RoBERTa; Top2Vec; machine learning; natural language processing; newspaper; sentiment analysis; topic modeling.

PubMed Disclaimer

Figures

FIGURE 1.
FIGURE 1.
Workflow of Research Methodology.
FIGURE 2.
FIGURE 2.
Preprocessing Steps Used in Cleaning Headlines.
FIGURE 3.
FIGURE 3.
RoBERTa Sentiment Classification Model Summary.
FIGURE 4.
FIGURE 4.
Topic 1 in UK’s Dataset - Maternity related news during pandemic.
FIGURE 5.
FIGURE 5.
Topic 5 in India’s Dataset - Vaccine Development related News.
FIGURE 6.
FIGURE 6.
Topic 4 in Japan’s Dataset - COVID-19 Cases in Different Prefectures.
FIGURE 7.
FIGURE 7.
Topic 10 in South Korea’s Dataset - Celltrion’s Vaccine Development related topic.
FIGURE 8.
FIGURE 8.
Overall and Various Topic’s Sentiment in UK’s Dataset.
FIGURE 9.
FIGURE 9.
Overall and Various Topic’s Sentiment in Indian Dataset.
FIGURE 10.
FIGURE 10.
Overall and Various Topic’s Sentiment in Japan’s Dataset.
FIGURE 11.
FIGURE 11.
Overall and Various Topic’s Sentiment in South Korean Dataset.
FIGURE 12.
FIGURE 12.
Comparison of Countries COVID-19 News Headlines Sentiments.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Wang C., Horby P. W., Hayden F. G., and Gao G. F., “A novel coronavirus outbreak of global health concern,” Lancet, vol. 395, no. 10233, pp. 470–473, Jan. 2020, doi: 10.1016/S0140-6736(20)30185-9. - DOI - PMC - PubMed
    1. Johns Hopkins Coronavirus Resource Center. COVID-19 Map. Accessed: Jan. 18, 2021. [Online]. Available: https://coronavirus.jhu.edu/map.html
    1. Krammer F., “SARS-CoV-2 vaccines in development,” Nature, vol. 586, no. 7830, pp. 516–527, Oct. 2020, doi: 10.1038/s41586-020-2798-3. - DOI - PubMed
    1. Forni G. and Mantovani A., “COVID-19 vaccines: Where we stand and challenges ahead,” Cell Death Differentiation, vol. 28, no. 2, pp. 626–639, Feb. 2021, doi: 10.1038/s41418-020-00720-9. - DOI - PMC - PubMed
    1. Reese S. D., “Prologue—Framing public life,” in Framing Public Life. Perspectives on Media and our Understanding of the Social World, Reese S. D., Gandy O. H., and Grant A. H., Eds. Mahwah, NJ, USA: Lawrance Erlbaum, 2001, pp. 7–31.

LinkOut - more resources