Incorporating Reviewer and Product Information for Review Rating Prediction

Date

2011-07

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

AAAI / International Joint Conferences on Artificial Intelligence Press, Menlo Park, California

Abstract

Traditional sentiment analysis mainly considers binary classifications of reviews, but in many real-world sentiment classification problems,nonbinary review ratings are more useful. This is especially true when consumers wish to compare two products, both of which are not negative. Previous work has addressed this problem by extracting various features from the review text for learning a predictor. Since the same word may have different sentiment effects when used by different reviewers on different products, we argue that it is necessary to model such reviewer and product dependent effects in order to predict review ratings more accurately. In this paper, we propose a novel learning framework to incorporate reviewer and product information into the text based learner for rating prediction. The reviewer, product and text features are modeled as a three-dimension tensor. Tensor factorization techniques can then be employed to reduce the data sparsity problems. We perform extensive experiments to demonstrate the effectiveness of our model, which has a significant improvement compared to state of the art methods, especially for reviews with unpopular products and inactive reviewers.

Description

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence

Keywords

BINARY DATA, PRODUCT REVIEWS, TENSOR ANALYSIS, MACHINE LEARNING, SENTIMENT ANALYSIS

Citation

Li, F., Liu, N., Jin, H., Zhao, K., & Yang, Q. (2011). Incorporating Reviewer and Product Information for Review Rating Prediction. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, ESP. (p.1820-1825). AAAI / International Joint Conferences on Artificial Intelligence Press. doi:10.5591/978-1-57735-516-8/IJCAI11-305

URI

http://hdl.handle.net/10625/49045

DOI

Collections

IDRC Research Results / Résultats de recherches du CRDI
2010-2019 / Années 2010-2019
Breaking the barriers to Internet access / Faire tomber les obstacles entravant l’accès à Internet