Review rating prediction based on the content and weighting strong social relation of reviewers

Abstract

Review rating is more helpful than review binary classification for many decision processes such as consumption decision-making, company product quality tracking and public opinion mining. In the review rating, reviewers are influenced not only by their own subjective feelings, but also by others' rating to the same product. Existing review rating prediction methods are mainly based on the content of reviews, which only consider the subjective factors of reviewers, but not consider the impact of other people in the social relations of reviewers. Based on it, we propose a review rating prediction method by incorporating the character of reviewer's social relations, as regularization constraints, into content-based methods. In addition, we further propose a method to classify the social relations of reviewers into strong social relation and ordinary social relation. For strong social relation of reviewers, we give higher weight than ordinary social relation when incorporating the two social relations into content-based methods. Experiments on two real movie review datasets demonstrate that the method of considering different social relations has better performance than the content-based methods and the method of considering social relations as a whole.