Sentiment Analysis of Public Transportation Services on Twitter Social Media Using the Method Naïve Bayes Classifier

Abstract

Public transportation services in Indonesia, especially Jabodetabek, have used social media, especially Twitter, as a way to improve services. Currently, the use of online transportation services is like a need; it is necessary to conduct a sentiment analysis of online transportation to find out how people respond to these online transportation services. This research was made to analyze community responses with data analysis in the form of tweets that filtered with a public transportation-related keyword then classified into positive and negative classes using the Naïve Bayes Classifier method. Based on the system built, the total sentiment results for the percentage of the occurrence of positive words were 0.507843137, and the sentiment results for the percentage of negative word occurrences were 1.4132493. The results show that the level of negative sentiment from public tweets is greater than the level of positive sentiment.