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

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