An entropy-based model for discovering the usefulness of online product reviews

E-commerce web sites, such as Amazon.com, provide platforms for consumers to review products and share their opinions. However, it is impossible for consumers to read throughout the huge amount of available reviews. In addition, the quality and helpfulness of reviews are unavailable unless consumers have to read through them. This paper proposes an entropy-based model to predict the helpfulness of reviews. Reviews can be ranked by our entropy-based scoring model and reviews that may help consumers better than others will be found. We also compare our model with several machine learning algorithms. Our experimental results show that our approach is effective in ranking and classifying online reviews. With the predicted helpfulness of reviews, consumers can make purchase decisions more easily.