Characterizing public emotions and sentiments in COVID-19 environment: A case study of India

ABSTRACT

Coronavirus 2019, or COVID-19, is a contagious disease triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With origins in Wuhan, China, this disease has since spread globally, resulting in the ongoing 2019–2020 coronavirus pandemic. As of May 3, 2020, the Ministry of Health and Family Welfare confirmed a total of 39,980 positive COVID-19 cases and 1,301 deaths in India (more than 3.42 million positive COVID-19 cases resulting in more than 243,000 deaths worldwide). To flatten the curve, India has been locking down its country from March 24 to May 17, 2020. This study collected “COVID-19 in India” related tweets (totaling 410,643 tweets in English) from March 22 to April 21, 2020 to gauge the unknowns and contexts associated with public sentiments during the lockdown. This work contributes to the growing body of studies on COVID-19 social media mining by extracting emotions and sentiments over time, which could potentially shed some lights on the contexts of expressions during pandemic.

KEYWORDS:

• COVID-19
• social media mining
• Twitter mining
• emotion mining
• sentiment analysis
• topic model

Acknowledgments

The authors like to thank Ly-Na Tran and Magdalena Theel for their initial review and feedback.

Declaration of interest statement

There is no conflict of interest.

Author contribution statement

The authors confirm the contribution to the paper as follows: study conception and design: Subasish Das, Anandi Dutta; data collection: Subasish Das; analysis and interpretation of results: Subasish Das, Anandi Dutta; draft manuscript preparation: Subasish Das, Anandi Dutta. All authors reviewed the results and approved the final version of the manuscript.

Additional information

Funding

This research was not supported by any funding.