Feature selection in sentiment analysis

CORIA 2012: Actes de la neuvieme COnference en Recherche d'Information et Applications - Proceedings of the 9th Conference on Information Retrieval and Applications

Volumn , Issue , 2012, Pages 273-284

  Kummer, Olena ^a   Savoy, Jacques ^a  

^a UNIVERSITY OF NEUCHÂTEL   (Switzerland)

Author keywords

Evaluation; Feature selection; Sentiment analysis; Text classification

Indexed keywords

ACCURACY RATE; CROSS VALIDATION; DATA SETS; EVALUATION; FEATURE SELECTION AND CLASSIFICATION; INFORMATION GAIN; SALIENT FEATURES; SENTIMENT ANALYSIS; SENTIMENT CLASSIFICATION; TEXT CLASSIFICATION; TEXT REPRESENTATION; WEIGHTING SCHEME; Z-SCORES;

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; INFORMATION RETRIEVAL;

DATA MINING;

EID: 84868543534     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Dave, K., Lawrence, S., & Pennock, D.M. (2003). "Mining the peanut gallery: Opinion extraction and semantic classification of product reviews". In Proceedings of the Twelfth International World Wide Web conference, pp. 519-528.
2. Forman, G. (2003). "An extensive empirical study of feature selection metrics for text classification". The Journal of Machine Learning Research, 3, pp. 1289-1305.
3. Liu, Y., Loh, H.T., Youcef-Toumi, K., & Tor, S.B. (2007). "Handling of imbalanced data in text classification: category-based term weights". Natural Language Processing and Text Mining, Springer, London, pp. 171-192.
4. Matsumoto, S., Takamura, H., & Okumura, M. (2005). "Sentiment classification using word sub-sequences and dependency sub-trees". In Proceedings PAKDD, pp. 301-311.
5. Mullen, T., & Collier, N. (2004). "Sentiment analysis using support vector machines with diverse information sources". In Proceedings EMNLP'04, pp. 412-418.
6. Muller, C. (1992). Principes et méthodes de statistique lexicale. Champion, Paris.
7. Paltoglou, G., & Thelwall, M. (2010). "A study of information retrieval weighting schemes for sentiment analysis", In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 1386-395.
8. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, vol. 2(1-2), pp. 1-135.
9. Pang, B., & Lee, L. (2005). "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, pp. 115-124.
10. Pang, B., & Lee, L. (2004). "A sentimental education: Sentiment analysis using subjectivity summarization based on Minimum Cuts". In Proceedings of the 42nd Annual Meeting of the Association of Computational Linguistics.
11. Pang, B., Lee, L., & Vaithyanathan S. (2002). "Thumbs up? Sentiment classification using machine learning techniques". In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79-86.
12. Raychev, V., Nakov, P. (2009). "Language-independent sentiment analysis using subjectivity and positional information". In Proceedings of the International Conference RANLP- 2009, pp. 360-364.
13. th ACM CIKM, pp. 625-631. (Pubitemid 44022238)
14. Yang, Y., & Pedersen, J.O. (1997). "A comparative study of feature selection in text categorization". In Proceedings ICML, pp. 412-420.
15. Zaidan. O.F., Eisner J., & Piatko, C.D. (2007). "Using annotator rationales to improve machine learning for text categorization". In Proceedings of NAACLHLT, pp. 260-267.
16. Zubaryeva, O., & Savoy, J. (2009). "Investigation in statistical language-independent approaches for opinion detection in English, Chinese and Japanese". In Proceedings of the Third International Workshop on Cross Lingual Information Access: Addressing the Information Need of Multilingual Societies, pp. 38-45.