Naïve Bayes and TF-IDF for Sentiment Analysis of the Covid-19 Booster Vaccine

Abstract

The booster vaccine polemic became a trending topic on Twitter and reaped many pros and cons. This booster vaccine began to be distributed on January 12, 2022. This booster vaccine program was implemented free of charge for the people of Indonesia to prevent the new variant of Covid-19, Omicron. The contribution of this study is to analyze the sentiment of booster vaccines to prevent covid-19 using the Naïve Bayes and TF-IDF methods. We conducted sentiment analysis to determine whether the tweet was positive, negative, or neutral. The solution used is the Naïve Bayes method and TF-IDF. The role of TF-IDF is to determine how relevant the data in the document is by utilizing word weighting. The stages of this research using CRISP-DM include Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment. The net data results show 1,557 data with a positive sentiment of 1,335, a neutral sentiment of 171 data, and a negative sentiment of 51 data. The test results with 60:40 data sharing obtained accuracy, precision, and recall values of 85.26%, 85%, and 100%. The results of this test have increased by 7.26%, 12%, and 20% from other previous studies with the same data distribution.

Keywords

• sentiment analysis
• naïve bayes
• vaccine booster