E-commerce Product rating Based on Customer Mining for Comments Using Machine Learning Technique
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Abstract
The E-commerce industry heavily relies on customer reviews to gauge product performance. In this paper, we leverage machine-learning techniques to analyze customer comments on a specific product. Our system employs data sets from Kaggle that include popular cell phones from around the world, classifying reviews as positive or negative. This approach helps sellers and company owners selling products online gain insight into customer satisfaction or dissatisfaction with their products. By analyzing customer feedback, businesses can improve their products and boost profits. To test our system, we used the Google Colab environment and experimented with three different algorithms: naïve bias, decision tree, and forest decision tree. The results indicated that the naïve bias algorithm had the highest accuracy (91.3%), precision (95.5%), recall (86.6%), and F-score (91%).
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