Sentiment Analysis of Twitter Data on Remote Learning Using Naïve Bayes Algorithm

Abstract

Covid-19 is widespread, resulting in a global pandemic. Distance Learning System (DLS) is considered as a solution but, the reality of the implementation of DLS is not in accordance with the expectations of the community. Many Twitter users wrote their opinions on DLS. The tendency of public sentiment can be used as a way to improve the existing education system in Indonesia and can be an input for the government in improving the DLS method that is being implemented. Thus, this study produced a system that can analyze tweet sentiment towards DLS. The tweet was obtained using the Twitter API. The method used is Naïve Bayes for the process of classification of positive, negative, and neutral sentiments using 600 data. Then, data sharing is done 80% data training and 20% data testing that will be in the text preprocessing first. The accuracy of sentiment analysis of DLS using the Naïve Bayes method using 3-fold Cross-Validation produces an average of 93%.

Sentiment Analysis of Internet Service Providers Using Naïve Bayes Based on Particle Swarm Optimization

“…They were collecting data through Twitter tweets using the Twitter API as much as 600 data. The accuracy using 3-fold Cross Validation results reached 93% (Khairina & Fitriati, 2021).…”