Sentiment Analysis of Mobile Legends Play Store Reviews Using Support Vector Machine and Naive Bayes - Alkhoze, Mona; Almasre, Miada (Journal of Digital Market and Digital Currency, 2025)
This study applies sentiment analysis to Mobile Legends Play Store reviews to classify user feedback as positive, negative, or neutral, offering insights into the factors influencing user satisfaction. Utilizing machine learning models—Naive Bayes and Support Vector Machine (SVM)—user sentiment is evaluated, and key themes in user feedback are identified. Both models demonstrate high accuracy, with SVM slightly outperforming Naive Bayes. Specifically, the SVM model records an accuracy of 84.95%, a precision of 81.76%, and an F1-score of 83.31%, while Naive Bayes achieves an accuracy of 84.10%, a precision of 82.09%, and an F1-score of 82.57%. This classification highlights a predominance of positive reviews, revealing players’ appreciation for the game's graphics and gameplay. In contrast, negative reviews expose common frustrations related to lag and technical issues, indicating areas for potential improvement. The analysis also uncovers the challenge of accurately classifying neutral sentiments due to the informal language and slang found in reviews written in Bahasa Indonesia. Future studies could address this by incorporating advanced NLP techniques, such as word embeddings or deep learning models, to better capture linguistic nuances. Overall, this research provides actionable insights for game developers, enabling them to prioritize updates and feature enhancements that align with player preferences and feedback trends.