COMPARISON OF THE PERFORMANCE OF SVM, RANDOM FOREST, AND NEURAL NETWORK ALGORITHMS IN SENTIMENT ANALYSIS OF OPENAI APPLICATION REVIEWS ON THE GOOGLE PLAY STORE

Ahmad Latif, Muhtyas Yugi, Fandy Setyo Utomo, Taqwa Hariguna

Abstract


This study compares the performance of three machine learning algo-rithms—Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN)—in sentiment analysis of user reviews for the OpenAI application on the Google Play Store. The primary objective of this study is to evaluate the effectiveness of each algorithm in clas-sifying user reviews into three sentiment categories: positive, negative, and neutral. The dataset used consists of user reviews of the OpenAI application, collected directly from the Google Play Store. Model per-formance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the Neural Network algorithm achieved the best overall performance in terms of accuracy and F1-score. SVM demonstrated competitive performance, particularly in classifying positive and neutral sentiments, while Random Forest showed an advantage in terms of precision but performed lower over-all, especially in classifying negative sentiments. Therefore, the Neural Network is considered the most effective algorithm for sentiment analysis tasks in this study

Keywords


Sentiment Analysis; Machine Learning; Support Vector Machine (SVM); Random Forest (RF); Neural Network (NN)

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DOI: https://doi.org/10.29100/jipi.v11i1.7793

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