Sentiment Analysis of Shopee User Reviews on the Play Store Using the Naive Bayes Algorithm for Evaluating User Satisfaction
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Abstract
The increasing number of Shopee user reviews on the Google Play Store serves as a valuable source for evaluating service quality; however, these reviews are unstructured, making manual analysis difficult. This study aims to analyze user sentiment toward the Shopee application using the Multinomial Naive Bayes method as an effort to evaluate user satisfaction. A total of 183 reviews were collected and processed through text preprocessing, including cleansing, case folding, tokenizing, stopword removal, and stemming. The textual features were then weighted using TF–IDF and classified into positive and negative sentiments. The experimental results show an accuracy of 83.87%, a sensitivity of 68.18%, and a specificity of 94.74%. Most negative sentiments were related to application bugs, delays, and login issues, while positive sentiments were dominated by reviews regarding product quality, pricing, and service speed. These findings indicate that the Naive Bayes method is effective for sentiment analysis of Indonesian-language reviews and can serve as a basis for developers to improve system stability and the overall user experience of the Shopee application.
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