Abstract
As online marketplaces have been popular during the past decades, online sellers and merchants ask their purchasers to share their opinions about the products they have bought. As a result, millions of reviews are being generated daily which makes it difficult for a potential consumer to make a good decision on whether to buy the product. Analyzing this enormous amount of opinions is also hard and time-consuming for product manufacturers. But in this prospering day of machine learning, going through thousands of reviews would be much easier if a model is used to polarize those reviews and learn from it. This thesis considers the problem of classifying reviews by their overall semantic (positive, negative, or neutral). To conduct the study different supervised machine learning techniques, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression have been attempted on products review dataset from Amazon. Their accuracies have then been compared.
Published Version
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