Sentiment Analysis of Bebiboo Educational Game Reviews Using Naïve Bayes for User Feedback Insights

Educational game applications play a significant role in enhancing children’s cognitive development by providing interactive and engaging learning experiences. This study aims to analyze user sentiment toward the Bebiboo educational game app, focusing on evaluating its educational value, ease of use, learning effectiveness, and user satisfaction. The research employs sentiment analysis using the Naïve Bayes algorithm to classify user reviews and WordCloud for visualizing common themes in feedback. A total of 1,024 Google Play reviews were extracted using Octoparse and processed to assess user opinions. The results show that most users express positive sentiment, particularly appreciating the app’s educational benefits and intuitive design. However, users raised concerns regarding persistent ads despite payment and limitations imposed by the rigid payment model. The Naïve Bayes algorithm accurately classified sentiments, providing valuable insights into user preferences and pain points. These findings suggest that Bebiboo developers can refine their monetization strategy, address user complaints, and improve the transparency of paid features. The study highlights the importance of sentiment analysis in improving educational game apps and offers a framework for future research in this domain. By addressing the identified issues, Bebiboo can enhance user experience, leading to higher user satisfaction and better learning outcomes.