Incorporate Reviewer and Product Information for Review Rating Prediction
Fangtao Li, Nathan Liu, Hongwei Jin, Kai Zhao, Qiang Yang and Xiaoyan Zhu
Among sentiment analysis tasks, review rating prediction is more helpful than binary (positive and negative) or ternary (positive, negative and neutral) classification, especially when the consumers want to compare two good products. Previous work has addressed this problem by extracting various features from the review text for learning a predictor. Since different reviewers may have different sentiment expression preferences for different products, we argue that the authored reviewer and the target product are helpful for the rating prediction task. In this paper, we propose a novel solution to incorporate reviewer and product information into the text based learner for rating prediction. The reviewer, product and text feature are modeled as a three-dimension tensor. The tensor factorization technique is employed to reduce the sparsity and complexity problems. The experiment results demonstrate the effectiveness of our model. We achieve significant improvement as compared with the state of the art methods, especially for the reviews with unpopular products and inactive reviewers.