Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Expanding domain sentiment lexicon through double propagation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Opinion formation under costly expression
ACM Transactions on Intelligent Systems and Technology (TIST)
The bag-of-opinions method for review rating prediction from sparse text patterns
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Collaborative inference of sentiments from texts
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Adaptive co-training SVM for sentiment classification on tweets
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Review rating prediction based on the content and weighting strong social relation of reviewers
Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing
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Traditional sentiment analysis mainly considers binary classifications of reviews, but in many real-world sentiment classification problems, non-binary 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.