Opinion Mining on COVID-19 Vaccination Tweets in Social Media Posts

Abstract

The COVID-19 pandemic has led to the loss of human life globally. It has changed our lives forever and is threatening to disrupt the global food system and the work universe. The goal of stopping the spread of COVID-19 is to vaccinate the public. However, there is uncertainty regarding the effectiveness of the vaccines. In this study, public tweets are related to the COVID-19 vaccinations have been analyzed using Natural language processing (NLP). In this paper, NLP libraries that are designed to find sentiments of people are presented. As such all the tweets related to the same topic can be easily identified. Sentiment is the expression that describes the mood of the people who are reacting to a tweet. It shows what the tweeter wants to convey and also gives a complete picture of how the COVID-19 vaccine is working. The sentiment of a tweet can be divided into three components: positive, neutral, and negative. This analysis helps in understanding the people's attitude towards COVID-19 vaccine. In this study, different libraries required to analyse the sentiment of people are discussed. Tweet posts from the Twitter API during the outbreak have been collected for analyzing the public reaction to different vaccines.