01875nas a2200253   4500000000100000000000100001008004100002260001200043653002400055653002100079653001700100653001400117100001700131700001600148700001600164700001900180700002400199245007900223856008100302300001100383490000600394520120700400022001401607       2021                            d  c06/202110aLogistic Regression10aMachine Learning10aNaïve Bayes10aMetamodel1 aPravin Kumar1 aMohit Dayal1 aManju Khari1 aGiuseppe Fenza1 aMariacristina Gallo00aNSL-BP: A Meta Classifier Model Based Prediction of Amazon Product Reviews  uhttps://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_10.pdf  a95-1030 v63 aIn machine learning, the product rating prediction based on the semantic analysis of the consumers' reviews is a relevant topic. Amazon is one of the most popular online retailers, with millions of customers purchasing and reviewing products. In the literature, many research projects work on the rating prediction of a given review. In this research project, we introduce a novel approach to enhance the accuracy of rating prediction by machine learning methods by processing the reviewed text. We trained our model by using many methods, so we propose a combined model to predict the ratings of products corresponding to a given review content. First, using k-means and LDA, we cluster the products and topics so that it will be easy to predict the ratings having the same kind of products and reviews together. We trained low, neutral, and high models based on clusters and topics of products. Then, by adopting a stacking ensemble model, we combine Naïve Bayes, Logistic Regression, and SVM to predict the ratings. We will combine these models into a two-level stack. We called this newly introduced model, NSL model, and compared the prediction performance with other methods at state of the art.  a1989-1660