Sentiment Analysis of Haylou Brand Bluetooth Earbuds Product Reviews on Marketplace Using Naive Bayes Algorithm - Aditya Mahatva Yodha; Ari Putra Wibowo (Journal of Artificial Intelligence and Software Engineering, 2026)
The growth of e-commerce marketplaces has increased the availability of customer reviews that can be utilized to determine user satisfaction with a product. However, manually analyzing a large number of reviews is inefficient and time-consuming. This study aims to classify customer sentiments toward Haylou Bluetooth earbuds based on marketplace reviews using the Naive Bayes algorithm. The dataset consisted of 151 customer reviews, including 127 positive reviews and 24 negative reviews. The research process involved data collection, text preprocessing, term weighting using Term Frequency-Inverse Document Frequency (TF-IDF), and sentiment classification using the Naive Bayes algorithm. Model evaluation was performed using Split Validation with an 80:20 ratio for training and testing data. The results showed that the proposed model achieved an accuracy of 80.00%. These findings indicate that the Naive Bayes algorithm has a fairly good capability in classifying customer sentiments toward Haylou Bluetooth earbuds based on marketplace review data.