Classification of Sentiment Tokopedia and Shopee App Reviews on Google Playstore Using Naive Bayes - Robait Tajuddin; Yudie Irawan; R. Rhoedy Setiawan (bit-Tech, 2025)
The rapid growth of e-commerce in Indonesia has led to an increase in user reviews that reflect satisfaction and experiences with applications such as Tokopedia and Shopee. The large volume of reviews makes manual analysis inefficient, thus requiring an automated method to identify user sentiments. This study aims to analyze and classify the sentiment of Tokopedia and Shopee reviews using the Naïve Bayes algorithm. The dataset consists of 10,000 Indonesian-language reviews collected from the Google Play Store. The analysis stages include data cleaning, stopword removal, stemming, and tokenization before classifying the reviews into positive and negative categories. The results show that the Naïve Bayes model performs well in sentiment classification. For Tokopedia data, the model achieved an accuracy of 81.84%, weighted precision of 84.44%, weighted recall of 81.84%, and weighted F1-score of 81.57%. Meanwhile, for Shopee data, the model performed better with an accuracy of 86%, weighted precision of 85.96%, weighted recall of 86%, and weighted F1-score of 83.25%. The word cloud visualization reveals that negative sentiments on Tokopedia are dominated by complaints about products and delivery, while on Shopee, they relate to late orders. Positive sentiments in both platforms highlight transaction convenience and affordable prices. These results demonstrate that Naïve Bayes is effective for sentiment analysis of e-commerce user reviews.