Review rating prediction framework using deep learning

Nowadays Review websites, such as Amazon and Yelp, allow users to post online reviews for several products, services and businesses. Recently online reviews play a great role in influencing the shopping decisions made by consumers. These reviews provide consumers with information and experience about product quality. Online reviews commonly comprise of a free-text format and user star-level rating Out of five. People believe that reviews will do help to the rating predication based on the idea that high star rating may significantly be attach with really good reviews. However, user’s rating star-level information is not usually available on many online review’s websites. Due to, it’s not possible for a given user to rate every product. On the other hand, most online reviews are written in free-text format, and therefore difficult for computer system to understand and analyze it. Identifying ratings for online reviews lately become an important topic in machine learning. In this paper, we propose a review rating prediction framework using deep learning. The framework consists of two phases based on deep learning bidirectional gated recurrent unit Bi-GRU model architectures, the first phase used for polarity prediction and the second phase used to predict review rating from review text. Extensive experiments were conducted to evaluate the proposed framework on two dataset Amazon and yelp datasets which are real-world datasets. The experimental results demonstrated that the proposed framework can significantly enhance the rating prediction in term of precision, recall, F1-score and root mean square root RMSE compared with the baseline approaches on different datasets.