Review Helpfulness Prediction Using Convolutional Neural Networks and Gated Recurrent Units

Abstract

Product reviews are one of the most important types of user-generated contents that are becoming more and more available. These reviews are valuable sources of knowledge for users who want to make purchasing decisions and for producers who want to improve their products and services. However, not all product reviews are equally helpful and this makes the process of finding helpful reviews among the massive number of similar reviews very challenging. To address this problem, automatic review helpfulness prediction systems are designed to classify reviews according to their content. In this study, a deep model is proposed to utilize content-based, semantic, sentiment, and metadata features of reviews for predicting review helpfulness. In the proposed method, convolution layer is used for learning feature maps and gated recurrent units are employed for exploiting sequential context. The results of comparing the proposed method with five traditional learning methods and two deep models trained on the same types of features shows that the proposed method outperforms other methods by 4% and 2% in terms of F1-measure and accuracy. Moreover, results reveal that both textual and metadata features are important in detecting helpful reviews. The findings of this study may help online retailers to efficiently rank the product reviews.