Tremendous amount of online reviews are posted every day on different online platforms which act as a valuable data reservoir for understanding customer satisfaction and analyzing customer's purchase intentions. Success largely depends on the retention of customers, which again depends on customer satisfaction (CS) levels, so understanding customer satisfaction is fundamental for the success of refurbishing products. Thus, this paper seeks to identify the features associated with customer satisfaction of refurbished mobiles by analyzing the online reviews collected from Amazon.com through ANN based prediction model. Later it was substituted with other classifiers namely Random Forest (RF), XG Boost (XGB), K-Nearest Neighbors (KNN) and Decision tree (DT) to assess the accuracy of our Refurbished Mobile Customer Satisfaction (RMCS) prediction model. As a result of analyzing 400,000 reviews on refurbished mobiles, it was found that ANN outperformed other classifiers in predicting RMCS.
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