Sentiment Classification on Indonesian Game Sequels: A Comparative Analysis of SVM and Naive Bayes on Coffee Talk Franchise Reviews - Nanda Yuris Riziq; Edy Mulyanto (Building of Informatics, Technology and Science (BITS), 2026)
User reviews on Steam are a critical source of feedback for game developers, yet manual sentiment analysis at scale is impractical. This study aims to compare Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), and Complement Naive Bayes (CNB) for binary sentiment classification and to analyze sequel reception patterns through cross-game evaluation. Reviews were preprocessed with negation-aware stopword removal and WordNet lemmatization, then vectorized with TF-IDF unigram and bigram features. Four scenarios were evaluated: two within-game baselines, a cross-game generalization, and a combined evaluation. Class imbalance was handled at the model level via class weighting for SVM and the CNB variant. Macro-averaged F1-Score was the primary metric. SVM consistently outperformed both Naive Bayes variants, achieving macro-F1 of 0.81 within-game and 0.75 cross-game. MNB collapsed to majority-class prediction across all scenarios; in S2, all three models also failed on the minority class due to the small test partition (n=6). The cross-game result indicates that sentiment patterns transfer reasonably from the original game to its sequel, with the performance drop concentrated in the minority class. These findings offer practical guidance for Indonesian game developers monitoring sequel reception through automated sentiment analysis.