Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Determining the semantic orientation of terms through gloss classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Introduction to Information Retrieval
Introduction to Information Retrieval
Collective Intelligence in Action
Collective Intelligence in Action
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Recommender systems are traditionally of following three types: content-based, collaborative filtering and hybrid systems. Content-based methods are limited in their applicability to textual items only, whereas collaborative filtering due to its accuracy and its black box approach has been used widely for different kinds of item recommendations. Hybrid method, the third approach, tries to combine content and collaborative approaches to improve the recommendation results. In this paper, we present an alternative approach to a hybrid recommender system that improves the results of collaborative filtering by incorporating a sentiment classifier in the recommendation process. We have explored this idea through our experimental work in movie review domain, with collaborative filtering doing first level filtering and the sentiment classifier performing the second level of filtering. The final recommendation list is a more accurate and focused set.