Analisis Sentimen Ulasan Aplikasi GoTube Menggunakan Naive Bayes Berbasis Particle Swarm Optimization

Abstract

This research employs the sentiment analysis of GoTube application reviews using Naïve Bayes based on Particle Swarm Optimization (PSO). The study focuses on addressing the challenge of efficiently managing and analyzing user comments in the development of the GoTube application. By implementing automated sentiment analysis using text mining techniques, developers can enhance user experience and save resources. The methodology involves data collection, preprocessing, feature extraction using TF-IDF, classification using Naïve Bayes, and evaluation with various parameters. Additionally, Particle Swarm Optimization is utilized for feature selection to enhance the performance of the Naïve Bayes Classifier. The study aims to contribute to the improvement of GoTube's service quality and user satisfaction.