Sentiment Analysis on User Reviews of Threads Applications in Indonesia

The primary goal of this study is to compare the analysis results of sentiment analysis using three different machine learning models: Naï ve Bayes, Support Vector Machine (SVM), and Random Forest.The raw dataset used for this study is sourced from the Google Play Scraper API, which is then preprocessed to ensure quality and accuracy of feature extraction.Once preprocessed, the machine divides the dataset for training and testing using the 80:20 rule.The results of this comparison provide insights into the strengths and weaknesses of each algorithm in the context of sentiment analysis of user reviews.This study aims to inform practitioners about the most effective techniques for extracting actionable insights from user-generated content on digital platforms.The evaluation shows that the Naï ve Bayes model achieved the highest accuracy of 81%, followed by the SVM model with 80%, and the Random Forest model with 76%.These findings highlight the Naï ve Bayes model as the most accurate for sentiment analysis in this context, with all models demonstrating robust performance.