Performance Comparison of Naïve Bayes, Random Forest, and Support Vector Machine Algorithms in Sentiment Analysis of the Weverse Application

Anisa Fitriyani, Ichsan Ibrahim

Abstract


The rapid development of digital technology has enhanced interactions between artists and fans through various community platforms, one of which is Weverse. User reviews on this platform reflect perceptions and satisfaction levels that are valuable for developers in improving service quality. However, the large volume of unstructured reviews creates challenges in systematically identifying opinion patterns. This study aims to analyze and compare the performance of Naïve Bayes, Random Forest, and Support Vector Machine (SVM) algorithms in classifying positive and negative sentiments from 5,000 Weverse user reviews collected from the Google Play Store through web scraping techniques. The data were processed through preprocessing stages, translated into English, and automatically labeled using VADER, while feature weighting was performed using the TF-IDF method. The experimental results indicate that Random Forest achieved the best performance with an accuracy of 83%, followed by SVM with 80.2% and Naïve Bayes with 74.2%. These findings confirm that Random Forest is more effective in handling high-dimensional text data in a stable and consistent manner. This study is expected to serve as a reference for the development of Indonesian-language sentiment analysis models on digital community platforms in future research.

Keywords


naïve bayes; random forest; sentiment analysis; support vector machine; weverse

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References


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DOI: https://doi.org/10.32520/stmsi.v15i2.5893

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