Comparative Study of Naive Bayes and SVM for E-Commerce Sentiment Classification on Shopee - Yoga Fradana; Cahyo Adi Nugraha; Frans Nicko Apriansyah; Tri Mutiara Illahi; Ken Ditha Tania; Allsela Meiriza (Universitas Budi Darma, 2026)
This study compares the performance of Naive Bayes and Support Vector Machine (SVM) for sentiment classification of men’s shirt product reviews on Shopee. A dataset of 500 reviews was collected via web scraping and processed through case folding, tokenizing, stopword removal, and stemming, followed by TF-IDF feature extraction. The data was split at an 80:20 ratio and evaluated using accuracy, precision, recall, and F1-score. The main contribution of this study is demonstrating that despite both algorithms achieving equal overall accuracy of 93%, SVM outperforms Naive Bayes in detecting negative sentiment on a class-imbalanced dataset, with SVM attaining a negative class recall of 0.87 and F1-score of 0.88 compared to 0.80 and 0.87 for Naive Bayes. These findings provide practical guidance for selecting an appropriate classifier in imbalanced e-commerce review classification tasks.