Sentiment Analysis on Canva Reviews Using Naive Bayes Method - Faiza Muhammad Julianto; Ahmad Turmudi Zy; Elkin Rilvani (University of Respati Yogyakarta, Indonesia, 2025)
Sentiment analysis for user review is a growing research topic in digital applications. In this study, we analyze user reviews from the Google Play Store to classify sentiments as positive or negative. The primary objective of this research is to evaluate the performance of the Naive Bayes classifier in sentiment classification. The methodology involves comprehensive data preprocessing, model training, and evaluation using performance metrics such as accuracy, precision, recall, and F1-score. The results indicate that the proposed model achieves an accuracy = 92%, precision = 85%, recall = 88%, and F1-score= 86%, respectively. These findings show the effectiveness of the proposed method that can extract valuable insights from user reviews to increase user satisfaction.