Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People’s Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage

Abstract

This study analyzed perceptions of Indians regarding COVID-19 booster dose vaccines using natural language processing techniques, particularly, sentiment analysis and topic modeling. We analyzed tweets generated by Indian citizens for this study. In late July 2022, the Indian government hastened the process of COVID-19 booster dose vaccinations. Understanding the emotions and concerns of the citizens regarding the health policy being implemented will assist the government, health policy officials, and policymakers implement the policy efficiently so that desired results can be achieved. Seventy-six thousand nine hundred seventy-nine tweets were used for this study. The sentiment analysis study revealed that out of those 76,979 tweets, more than half (n = 40,719 tweets (52.8%) had negative sentiments, 24,242 tweets (31.5%) had neutral sentiments, and 12,018 tweets (15.6%) had positive sentiments. Social media posts by Indians on the COVID-19 booster doses have focused on the feelings that younger people do not need vaccines and that vaccinations are unhealthy.

Keywords

• booster dose
• COVID-19
• NLP
• topic modeling
• sentiment analysis