Detecting Opinion Sentences Specific to Product Features in Customer Reviews using Typed Dependency Relations

International Workshop on Events in Emerging Text Types, eETTs 2009 - held in conjunction with the International Conference RANLP 2009, Proceedings

Volumn , Issue , 2009, Pages 38-43

  Qadir, Ashequl ^a  

^a UNIVERSITY OF WOLVERHAMPTON   (United Kingdom)

Author keywords

customer reviews; frequent word association; opinion sentences; Product features; typed dependency relations

Indexed keywords

AUTOMATION; NATURAL LANGUAGE PROCESSING SYSTEMS;

CUSTOMER REVIEW; DEPENDENCY RELATION; FACTUAL INFORMATION; FREQUENT WORD ASSOCIATION; OPINION SENTENCE; PRESENCE OF PRODUCT; PRODUCT FEATURE; PRODUCT REVIEWS; TYPED DEPENDENCY RELATION; WORD-ASSOCIATION;

SALES;

EID: 85010194252     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. J. Wiebe. Learning Subjective Adjectives from Corpora. In Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, 2000.
2. V. Hatzivassiloglou and J. Wiebe. Effects of Adjective Orientation and Gradability on Sentence Subjectivity. In Proceedings of the 18th conference on Computational linguistics, Germany, 2000.
3. P. D. Turney. Thumbs up or thumbs down?: Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, 2002.
4. N. Godbole, M. Srinivasaiah and S. Skiena. Large-scale Sentiment Analysis for News and Blogs. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM), 2007.
5. S.-M. Kim and E. Hovy. Automatic Detection of Opinion Bearing Words and Sentences. In Companion Volume to the Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP), 2005.
6. E. Riloff and J. Wiebe. Learning Extraction Patterns for Subjective Expressions. In Proceedings ofthe Conference on Empirical Methods in Natural Language Processing (EMNLP), 2003.
7. J. Wiebe, T. Wilson and M. Bell. Identifying Collocations for Recognizing Opinions. In Proceedings of the ACL Workshop on Collocation: Computational Extraction, Analysis, andExploitation, Toulouse, France, 2001.
8. Hong Yu and Vasileios Hatzivassiloglou. Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences. In Proceedings ofthe Conference on Empirical Methods in Natural Language Processing (EMNLP), 2003.
9. T. Wilson, J. Wiebe and R. Hwa. Just How Mad are You? Finding Strong and Weak Opinion Clauses. In Proceedings ofAAAI-04, 21st Conference of the American Association for Artificial Intelligence, 2004.
10. Z. Fei, X. Huang and L. Wu. Mining the Relation between Sentiment Expression and Target Using Dependency of Words. In proceedings of 20th Pacific Asia Conference on Language, Information and Computation (PACLIC20), Wuhan, China, 2006.
11. J. Yi, T. Nasukawa, R. Bunescu and W. Niblack. Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2003.
12. J. Yi and W. Niblack. Sentiment Mining in WebFountain. In Proceedings of the International Conference on Data Engineering (ICDE), 2005.
13. M. Hu and B. Liu. Mining Opinion Features in Customer Reviews. In Proceedings ofAAAI, San Jose, USA, 2004.
14. A.-M. Popescu, and O. Etzioni. Extracting Product Features and Opinions from Reviews". In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), Vancouver, British Columbia, Canada, 2005.
15. R. Ghani, K. Probst, Y. Liu, M. Krema and A. Fano. Text Mining for Product Attribute Extraction. SIGKDD Explorations Newsletter, 8(1). 2006.
16. A. Qadir. Identifying Frequent Word Associations for Extracting Specific Product Features from Customer Reviews. In Proceedings of International Symposium on Data and Sense Mining Machine Translation and Controlled Languages, and their Application to Emergencies and Safety Critical Domains, Besancon, France, 2009.
17. M.-C. de Marneffe and C D. Manning. The Stanford typed dependencies representation. In COLING Workshop on Cross-framework and Cross-domain Parser Evaluation, 2008.
18. M.-C. de Marneffe and C D. Manning. Stanford Typed Dependencies Manual. Technical report, 2008.
19. L. Zhuang, F. Jing and X. Zhu. Movie Review Mining and Summarization. In Proceedings of ACM Conference on Information and Knowledge Management (CIKM), Arlington, Virginia, USA, 2006.
20. T. H. King, R. Crouch, S. Riezler, M. Dalrymple and R Kaplan. The PARC 700 Dependency Bank. In 4th International Workshop on Linguistically Interpreted Corpora (LINC-03), 2003.