Foundations of statistical natural language processing
Foundations of statistical natural language processing
Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Query Expansion with Long-Span Collocates
Information Retrieval
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Recognizing subjectivity: a case study in manual tagging
Natural Language Engineering
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
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
Exploring evidence for shallow parsing
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
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
Automatic construction of an opinion-term vocabulary for ad hoc retrieval
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
On performance of topical opinion retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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With a growing amount of subjective content distributed across the Web, there is a need for a domain-independent information retrieval system that would support ad hoc retrieval of documents expressing opinions on a specific topic of the user's query. In this paper we present a lightweight method for ad hoc retrieval of documents which contain subjective content on the topic of the query. Documents are ranked by the likelihood each document expresses an opinion on a query term, approximated as the likelihood any occurrence of the query term is modified by a subjective adjective. Domain-independent user-based evaluation of the proposed method was conducted, and shows statistically significant gains over the baseline system.