Classification of opinions with non-affective adverbs and adjectives

International Conference Recent Advances in Natural Language Processing, RANLP

Volumn , Issue , 2009, Pages 421-427

  Sokolova, Marina ^a   Lapalme, Guy ^b  

^a CHILDREN S HOSPITAL OF EASTERN ONTARIO   (Canada)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

REVIEWS;

AFFECTIVE WORDS; CONSUMER GOODS; DATA-DRIVEN METHODS; DOMAIN INDEPENDENTS; LANGUAGE PATTERNS; LEGAL DOCUMENTS; PRACTICAL USE;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84866862037     PISSN: 13138502     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. R. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6:1817-1853, 2005.
2. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representataions for domain adaptation. In Neural Information Processing Systems, 2006.
3. F. Benamara, C. Cesarano, A. Picariello, D. Reforgiato, and V. Subrahmanian. Sentiment analysis: Adjectives and adverbs are better than the adjectives alone. In Proceedings of International Conference on Weblogs and Social Media (ICWSM'2007), 2007.
4. D. Biber, S. Johansson, G. Leech, S. Conrad, and E. Finegan. Longman Grammar of Spoken and Written English. Longman, 1999.
5. J. Blitzer, M. Dredze, and F. Pereira. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 440-447. Association for Computational Linguistics, 2007.
6. D. Bolinger. Degree Words. Mouton De Gruyter, 1972.
7. E. Breck, Y. Choi, and C. Cardie. Identifying expressions of opinion in context. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-2007), pages 2683-2688, 2007.
8. O. Feiguina and G. Lapalme. Query-based summarization of customer reviews. In Proceedings of the 20th Canadian Conference on Artificial Intelligence ( AI'2007), pages 452-463, 2007.
9. M. Hamilton. Extending an information processing model of language intensity effects. Journal of Language and Social Psychology, 17:109-143, 1998.
10. M. Hu and B. Liu. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conf on Knowledge Discovery and Data Mining (KDD'04), pages 168-177, 2004.
11. W. Kamakura, S. Basuroy, and P. Boatwright. Is silence golden? an inquiry into the meaning of silence in professional product evaluations. Quantitative Marketing and Economics, 4:119-141, 2006.
12. S.-M. Kim and E. Hovy. Crystal: Analyzing predictive opinions on the web. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLPCoNLL), pages 1056-1064, 2007.
13. G. Leech, P. Rayson, and A. Wilson. Word Frequencies in Written and Spoken English: based on the British National Corpus. Longman, 2001.
14. B. Loken. Consumer psychology: Categorization, inferences, affect, and persuasion. Annual Review of Psychology, 57:453-485, 2006.
15. V. Ng, S. Dasgupta, and S. M. N. Arifin. Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages 611-618. Association for Computational Linguistics, 2006.
16. K. Nigam and M. Hurst. Towards a robust metric of opinion. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text, pages 98-105. AAAI, 2004.
17. B. Pang and L. Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115-124. Association for Computational Linguistics, 2005.
18. A. Popescu and O. Etzioni. Extracting product features and opinions from reviews. In Proceedings of HLTC/EMNLP 2005, pages 339 - 346, 2005.
19. E. Riloff, S. Patwardhan, and J. Wiebe. Feature subsumption for opinion analysis. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 440-448. Association for Computational Linguistics, 2006.
20. roget. Roget's interactive thesaurus, 2006. http://thesaurus.reference. com/.
21. M. Sokolova and G. Lapalme. Verbs speak loud: Verb categories in learning polarity and strength of opinions. In Proceedings of the 21st Canadian Conference on Artificial Intelligence (AI'2008), pages 320-331. Springer, 2008.
22. M. Sokolova and G. Lapalme. Learning opinions without using emotional words. In Proceedings of the 22nd Canadian Conference on Artificial Intelligence (AI'2009), pages 253-256. Springer, 2009.
23. T. Wilson, J. Wiebe, and R. Hwa. Recognizing strong and weak opinion clauses. Computational Intelligence, 22(2):73-99, 2006.