Incorporating Reviewer and Product Information for Review Rating Prediction
Date
2011-07
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
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Conference Paper
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Text
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