Comparison of Naïve Bayes Algorithm with Genetic Algorithm and Particle Swarm Optimization as Feature Selection for Sentiment Analysis Review of Digital Learning Application

• Published in: Journal of physics. Conference series 2020-11, Vol.1641 (1), p.12040
• Main Authors: Ernawati, Siti, Wati, Risa, Nuris, Nuzuliarini, Marita, Lita Sari, Yulia, Eka Rini
• Format: Article
• Language: English
• Subjects: Classification; Data mining; Feature selection; Genetic algorithms; Labels; Machine learning; Particle swarm optimization; Physics; Sentiment analysis
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1641/1/012040

The problem examined in this study is about the user's trust in using digital learning applications that are downloaded on playstore. Many reviews are given by the public about the application that has been downloaded on playstore. This review is very influential on their trust in using the application. The purpose of this study is to classify data according to labels and find out the best choice between the classification method and the proposed selection feature as a consideration in determining the use of digital learning applications. This study compares the classification method, the Naïve Bayes algorithm and the genetic algorithm (GA) as feature selection with the Naïve Bayes algorithm classification method and the particle swarm optimization (PSO) as feature selection to categorize the reviews in the playstore. The experimental results show that the Naïve Bayes algorithm and PSO as feature selection is the best model between the two models proposed in this study. Reviews can be classified into positive and negative labels well. The accuracy is 98.00%. The results of the classification are expected to help in making decisions when going to use digital learning application.