@article{article_1596100, title={Sentiment Analysis and Automatic Response Generation for E-Commerce Comments}, journal={Natural and Applied Sciences Journal}, volume={8}, pages={18–25}, year={2025}, DOI={10.38061/idunas.1596100}, author={Macit, Ayşe and Postalcıoğlu, Seda}, keywords={Duygu Analizi, Metin Madenciliği, Otomatik Sınıflandırma}, abstract={This study addresses the use of machine learning techniques for automatic classification of product reviews on e-commerce sites and generating appropriate responses. It was carried out with approximately 15,000 data labeled as positive, negative and neutral obtained from the "E-Commerce Product Reviews" data set. The TF-IDF vectorization method, which is a text mining technique, was used in the study. Multinomial Naive Bayes, Support Vector Machine, Random Forest, Logistic Regression techniques were used for sentiment analysis. As a result of the studies, the accuracy values of Multinomial Naive Bayes, Support Vector Machine, Random Forest, Logistic Regression algorithms show successful results as 87%, 88%, 85% and 88%, respectively. As a result, it was concluded that automatic comment analysis tools can be effective in improving customer relations for e-commerce sellers.}, number={1}, publisher={İzmir Demokrasi Üniversitesi}