Optimizing Sentiment Classification of E-Commerce Product Reviews: A Comparative Study of Naïve Bayes and SVM with SMO - Riki; Sonya Eliesse Dameria; Aditiya Hermawan; Junaedi; Yusuf Kurnia (Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto, 2025)
The rapid growth of e-commerce has led to a surge in user-generated product reviews, making manual sentiment analysis impractical. This study explores automated sentiment classification using two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM) that is optimized with Sequential Minimal Optimization (SMO). The dataset comprises 2,000 Shopee product reviews that are labeled as positive, neutral, or negative. The study focuses on assessing the effectiveness of these algorithms in classifying product reviews, especially in the diverse and high-volume data that is typically on e-commerce environments. Empirical evaluation shows that Naïve Bayes achieves 68% accuracy, while SVM with SMO attains 79%. Additionally, the study evaluates other important performance metrics, such as precision, recall, and F1-score. This study show that SVM with SMO outperforms Naïve Bayes in accurately classifying product reviews. These findings highlight the superior capability of SVM with SMO in handling complex sentiment data, thereby offering a more robust foundation for automated review classification. This research provides insights into selecting suitable classifiers for improving customer experience and strategic decision-making in digital commerce.