Review Rating Prediction Based on User Context and Product Context

Abstract

With the explosion of online user reviews, review rating prediction has become a research focus in natural language processing. Existing review rating prediction methods only use a single model to capture the sentiments of review texts, ignoring users who express the sentiment and products that are evaluated, both of which have great influences on review rating prediction. In order to solve the issue, we propose a review rating prediction method based on user context and product context by incorporating user information and product information into review texts. Our method firstly models the user context information of reviews, and then models the product context information of reviews. Finally, a review rating prediction method that is based on user context and product context is proposed. Our method consists of three main parts. The first part is a global review rating prediction model, which is shared by all users and all products, and it can be learned from training datasets of all users and all products. The second part is a user-specific review rating prediction model, which represents the user’s personalized sentiment information, and can be learned from training data of an individual user. The third part is a product-specific review rating prediction model, which uses training datasets of an individual product to learn parameter of the model. Experimental results on four datasets show that our proposed methods can significantly outperform the state-of-the-art baselines in review rating prediction.

Keywords

• sentiment classification
• review texts
• user-specific model
• product-specific model