Sentiment analysis on vaccine COVID-19 using word count and Gaussian Naïve Bayes

Abstract

Since the Coronavirus disease 2019 (COVID-19) pandemic hit the world, it had a significant negative impact on individuals, governments, and the global economy. One way to reduce the negative impact of COVID-19 is to vaccinate. Briefly, vaccination aims to enable the formed immune system to remember the characteristics of the targeted viral pathogen and be able to initiate an immune response that is rapid and strong enough to defeat future live viral pathogens. However, there are still many people in the world who are anti-vaccine. This certainly greatly hampers the process of accelerating the formation of the body's immune system widely in the community. Anti-vaccine people can be found on various social media platforms. Twitter was chosen as the data source because twitter is a common source of text for sentiment analysis. This study aims to analyze public sentiment on the COVID-19 vaccine through twitter in the form of tweets and retweets. This study uses the Gaussian Naïve Bayes method to see the results of the classification of sentiment analysis. The results obtained based on experiments prove that the Gaussian Naïve Bayes method can produce an average accuracy of 97.48% for each vaccine dataset used.