Social Media Data-Driven Sentiment Analysis for COVID-19 and COVID-19 Vaccines

Abstract

In this paper, we analyze social media data (e.g., tweets) related to coronavirus disease 2019 (COVID-19) and COVID-19 vaccines. The main objective is to explore daily COVID-19 cases and vaccine rates in addition to analyzing sentiments and discussions related to COVID-19 vaccination on social media, e.g., Twitter. During the early days of the pandemic, there were rapid developments of vaccines that can prevent the novel COVID-19. However, the potential hurdles of developing COVID-19 vaccines faster than any other conventional vaccine has made some people apprehensive about taking the COVID-19 vaccine. Since social media keeps individuals connected locally and globally, Twitter as a social networking platform is a great way to collect information on tweets related to the coronavirus vaccine. Specifically, this paper studies various data analytic tools that can help study the changes in users’ opinions and emotions related to coronavirus vaccines, as well as studying the coronavirus cases and vaccine rates globally. Furthermore, this study will enable individuals to get real-time insights into the sentiments of COVID-19 vaccines based on social media tweets.