MINECRAFT REVIEW SENTIEMNT CLASSIFICATION ON THE GOOGLE PLAY STORE: A COMPARATIVE STUDY OF NAÏVE BAYES AND RANDOM FOREST
KLASIFIKASI SENTIMEN ULASAN MINECRAFT PADA GOOGLE PLAY STORE: STUDI KOMPARATIF NAÏVE BAYES DAN RANDOM FOREST
DOI:
https://doi.org/10.21070/ups.10666Keywords:
Sentiment Analysis, Naïve Bayes, Random Forest, Google Play Store, MinecraftAbstract
The growth of mobile apps has increased the volume of user reviews on the Google Play Store, especially for gaming apps. These reviews provide valuable insights into user assessments of app quality. This study focuses on sentiment classification of Minecraft reviews by comparing the performance of Naïve Bayes and Random Forest algorithms. Data were collected through scraping, yielding 12,691 reviews, with 11,078 valid after preprocessing. Classification used TF-IDF weighting with three data splits: 90:10, 80:20, and 70:30. Evaluation metrics included accuracy, precision, recall, F1-score, and AUC. The results show that Naïve Bayes consistently outperformed Random Forest across all scenarios. The best performance was achieved with a 90:10 split, reaching an accuracy of 0.9052 and an F1-score of 0.8993. These findings indicate that Naïve Bayes is more optimal for sentiment classification of text-based game reviews.
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