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Amazon.com, along with several other commercial websites for products and services, provides a platform for consumers to share their opinions by providing reviews and answering product related questions (QA data). These opinions can be quantitative, qualitative or a combination of both. Owing to the large corpus of such data available, there are several learning and classification approaches available to scrutinize them e.g., those based on Entropy measures, machine learning, stochastic, and natural language processing etc. In this paper, we review some of the prominent techniques and explore a hybrid approach, involving Entropy, Bilinear and statistical measures, to use heterogeneous consumer data and simultaneously analyze and rank products for customers. With experimental results, we show that our approach effectively ranks products using (1) text reviews (2) QA data and (3) star rating of products. We also make a case that the ranks calculated are more relevant to the customers and can enable better prediction on the products sale for the sellers. Keywords-product reviews, product ranking, similarity, classification

An Entropy based Product Ranking Algorithm using Reviews and Q and A Data