In March 2020, Coronavirus disease was officially announced as a pandemic all over the world by the World Health Organization (WHO). Since then, the whole pharmaceutical world is in a state of war with COVID-19 and has a responsibility to provide its vaccine for the entire world as soon as possible. People have come forward and spoken about their emotions on both the mainstream and the social media. This study aims to analyze COVID-19 Vaccine related tweets and generate a report on that analysis. The motivation behind this study is the overspread of the virus and the increasing number of cases daily. The method used for topic modeling is a latest statistical model in Natural Language Processing (NLP), and it belongs to the machine learning toolbox and artificial intelligence toolbox, Latent Dirichlet Allocation (LDA) to determine popular themes, and for sentiment analysis, VADER and TextBlob are used on the dataset of 980,557 tweets which were scraped from Twitter based on a particular set of 14 keywords namely, 'Covaxin,' 'BNT162b2', 'SputnikV,' and 'mRNA-1273' from November 1 to December 16 in 2020. © 2021 IEEE.
Analysing Public Sentiments Regarding COVID-19 Vaccine on Twitter