IRHM: Inclusive Review Helpfulness Model for Review Helpfulness Prediction in E-commerce Platform

Online reviews have become essential aspect in E-commerce platforms due to its role for assisting customers’ buying choices. Furthermore, the most helpful reviews that have some attributes are support customers buying decision; therefore, there is needs for investigating what are the attributes that increase the Review Helpfulness (RH). This research paper proposed novel model called inclusive review helpfulnessmodel (IRHM) can be used to detect the most attributes affecting the RH and build classifier that can predict RH based on these attributes. IRHM is implemented on Amazon.com using collection of reviews from different categories. The results show that IRHM can detect the most important attributes and classify the reviews as helpful or not with accuracy of ۹۴%, precision of ۰.۲۰ and had excellent area under curve close to ۰.۹۴.

کلیدواژه ها:

Review helpfulness ، Recommender System ، Machine learning ، Sentiment analysis