Covid-19 Vaccine Tweets Sentiment Analysis and Topic Modelling for Public Opinion Mining

Abstract

The world is facing the major crisis in the form of coronavirus pandemic. Since it’s been more than a year of Covid-19 pandemic, there has been a significant call in social media regarding the requirement and feasibility for COVID-19 Vaccine. This paper aims at analyzing tweets related to Covid-19 Vaccine, determining the sentiments about vaccination and extracting the significant topics. We performed multi-class sentiment analysis, steps comprising of pre-processing followed by training three different classification models: Gaussian Naïve Bayes, Support Vector Machine and LSTM. Results of the model obtained was one the three (Positive, Negative, Neutral) sentiment. Based on the outcomes, accuracy and F1- scores were computed to draw comparison between distinct models. Topic Modeling was performed using LDA on the combined tweets dataset to derive top seven important topics. In addition, Exploratory Data Analysis was also performed on dataset consisting of Vaccination Progress worldwide to bring out popularity of vaccines.