Sentiment Analysis of Shopee App User Reviews Based on Naïve Bayes Classifier - Asmiran, Atika Andrian; Nassa, Azzka Farikatun; Layinah, Ayuni; warto, Warto (Universitas Muhammadiyah Pekajangan Pekalongan, 2025)
Technological developments have made various activities easier, including shopping. Through applications, users can make transactions without having to meet face-to-face with sellers. Although it provides convenience, this service does not always run perfectly. One example of a well-known e-commerce application in Indonesia is Shopee. This study analyzes the Shopee application because it significantly supports the Indonesian economy through digitizing trade. Data from 3,000 Shopee reviews obtained from the Google Play Store website in 2025 were analyzed using Natural Language Processing with the Naïve Bayes method, classifying reviews into positive and negative sentiment categories. From the labeling results, text from each sentiment category was extracted to generate essential and valuable information for decision-making. The sentiment classification using the Naïve Bayes Classifier achieved an accuracy rate of 81.30%. The findings of this study can serve as a foundation for enhancing customer satisfaction and comfort, as well as improving the quality of Shopee’s products and services, in line with consumer expectations.