Indonesian Twitter Sentiment Analysis Application on The Covid l9 Vaccine Using Naive Bayes Classifier

Abstract

It's even one year since the COVID-19 pandemic hit Indonesia, to anticipate it, the government brought in a COVID-19 vaccine. Various types of COVID-19 vaccine have been introduced to Indonesia, including which ones will be considered the best according to the community through the Twitter platform. One of the venues that creates the most public sentiment is Twitter. It can be determined whether the public fully approves or rejects the existence of vaccination in Indonesia by analyzing public sentiment surrounding the COVID-19 vaccine. Data acquisition using a crawling procedure by connecting the Twitter API, pre-processing, sentiment categorization, and sentiment analysis outcomes are the stages of the sentiment analysis process to become a sentiment analysis application. The PHP and MySQL programming languages are used to create the database for the sentiment analysis application. After the application has been fully implemented, it can do sentiment analysis from each dictionary probability using the Naive Bayes Classifier approach. The study of the two keywords "vaksin covid" and "vaksin corona" yielded the following results. It has 93% positive sentiment results, 72% negative sentiment results, and 35% neutral sentiment outcomes, with an accuracy of 94.74% and 75.47% per keyword. Meanwhile, the Sinopharm vaccine, which has the most positive attitude with the terms "vaksin sinovac," "vaksin astrazeneca," "vaksin sinopharm," and "vaksin nusantara," has 84 percent tweets with a 74.23% accuracy rate.