SENTIMENT ANALYSIS OF INTERNET SERVICE PROVIDERS USING NAÏVE BAYES BASED ON PARTICLE SWARM OPTIMIZATION

Abstract

Twitter is a social media application that is widely used. Where as many as 18.45 million users in Indonesia, Twitter users can send and read messages with a maximum of 280 characters displayed. Many opinions and reviews uploaded by users via tweets on social media are experienced in everyday life. Lately, comments about internet service providers in the covid-19 pandemic have been widely reviewed by Twitter users. Problems about internet providers through words often uploaded include internet provider complaints related to network quality, package prices, user satisfaction, and others. This study aims to classify Twitter users' tweets against internet service providers in Indonesia by analyzing the sentiments of 3 internet service providers, namely with the keywords Biznet, first media, and Indihome, using the Naïve Bayes algorithm and optimization with Particle Swarm Optimization. This research is also helpful in helping to become a measure where prospective new users will see the quality of an internet service provider in Indonesia through tweets and then divide the opinion into positive and negative. The results of Biznet's research using Naïve Bayes produce an accuracy of 77.94%, and after optimization, it becomes 81.62%. First media using Naïve Bayes produces 91.39% accuracy, and after optimization, it becomes 92.88%. Indihome using Naïve Bayes produces an accuracy of 85.78%, and after optimization, it becomes 87.48%. It can be concluded that the Naïve Bayes algorithm is a good algorithm for classification, and optimization using Particle Swarm Optimization has an effect on increasing accuracy results