Abstract
Ratings and product reviews could be considered as one of the main features determining the quality of a product in online store systems, especially in deciding whether to place a product as part of an online store's inventory. Online vendors are attracted by product reviews and ratings in order to study on potential products and related predictions. In this way, different machine learning algorithms such as Support Vector Machine, Bayesian Networks, Random Forests and Logistic Regression are investigated. The performance of each model is evaluated using accuracy, sensitivity and F1 score on the data from amazon online store website, 1996 to 2014. It is noteworthy to mention that the results of this paper can be used as an initial input to long-term product rating predictions. Keywords : Rating, Machine Learning Algorithm, Text mining, Classification, Resampling DOI : 10.7176/CEIS/10-5-03 Publication date :June 30 th 2019
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