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 (The IJICS (International Journal of Informatics and Computer Science), 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.