APPLICATION OF LEXICON BASED FOR SENTIMENT ANALYSIS OF COVID-19 BOOSTER VACCINATIONS ON TWITTER SOCIAL MEDIA USING NAÏVE BAYES METHOD

Abstract

To combat the Covid-19 epidemic, the government issues laws governing vaccination implementation. Health Minister Number Ten of 2021 issued the regulation. This program raises advantages and disadvantages, necessitating examination through feedback. The opinions and narratives that individuals share on social media sites like Twitter can be used to get feedback. This work seeks to construct a model to assess public opinion of the Covid-19 Booster Vaccination by using the Lexicon Based technique to identify sentiment on tweet data. Naïve Bayes and logistic regression are the classification techniques employed in this study. The comparison of the two methods' findings reveals that Logistic Regression, with an accuracy of 72%, is superior to Naïve Bayes, which has an accuracy of 70%. There were 607 tweet messages from Twitter that were processed. From January 1 to July 30, 2022, the model was tested for its ability to interpret public opinion on Twitter. The model found that people's attitudes toward the COVID-19 booster shot tended to be favorable. It can be developed by including datasets for additional research. For further research, it can be developed by adding datasets.