Sentiment Analysis and Automatic Response Generation for E-Commerce Comments

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Natural and Applied Sciences Journal
Cilt: 8 Sayı: 1
Sentiment Analysis and Automatic Response Generation for E-Commerce Comments

Sentiment Analysis and Automatic Response Generation for E-Commerce Comments

Authors : Ayşe Macit, Seda Postalcıoğlu

Pages : 18-25

Doi:10.38061/idunas.1596100

View : 40 | Download : 42

Publication Date : 2025-06-30

Article Type : Research Paper

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.
Keywords : Duygu Analizi, Metin Madenciliği, Otomatik Sınıflandırma

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