Analyzing Customer Sentiments from Product Reviews Using Deep Learning Algorithms in E-Commerce Web Applications
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Abstract
Sentimental Analysis is utilized in all the online product companies’ reviews. Other users had these reviews considered as they searched products. This study focuses on analyzing customer sentiments from product reviews using supervised machine learning and deep e-commerce Web application learning algorithms. The review of the Amazon product used a balanced data set of 400,000 reviews of fifty thousand products over five product categories including: mobile electronics, furniture, cameras, groceries, and watches. A preprocessing pipeline of the reviews went through a preprocessing stage of data cleaning and tokenization followed by Features extracted using Bag-of-Words (Bow) and TF-IDF approaches. As a set of models, the employed BERT, a transformer-based deep learning model, Naive Bayes, and Support Vector Machines (SVMs). Among them, BERT's F1-score of 92.41, recall of 92.24, accuracy of 92.14, and precision of 92.14 were the best. The results validate BERT's superior capability in capturing contextual and sentiments, making it a powerful tool for automating sentiment analysis and enhancing user experience in e-commerce platforms.
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