지금 저희 연구실에서도 비슷한걸 해서 하고 있습니다.

아래 논문들을 참고하면 괜찮을 듯 합니다.
우리 나라에서는 거의 논문이 나오지 않아서 한글에서 발생하는 문제들이 다루어지지 않아서 좀 어렵지만, 그래도 많이 도움이 될 듯 합니다.


[1] Ana-Maria Popescu, Oren Etzioni: Extracting Product Features and Opinions from Reviews. HLT/EMNLP 2005
[2] Appraisal Analysis, http://www.grammatics.com/appraisal/index.html
[3] BOIY, Erik, HENS, Pieter, DESCHACHT, K., MOENS, Marie-Francine, Automatic Sentiment Analysis of On-line Text. In Proceedings of the 11th International Conference on Electronic Publishing, Openness in Digital Publishing: Awareness, Discovery & Access, June 13-15, 2007, Vienna Austria
[4] Casey Whitelaw, Navendu Garg, Shlomo Argamon: Using appraisal groups for sentiment analysis. CIKM 2005: 625-631
[5] Christopher Scaffidi, Kevin Bierhoff, Eric Chang, Mikhael Felker, Herman Ng, Chun Jin: Red Opal: product-feature scoring from reviews. ACM Conference on Electronic Commerce 2007: 182-191
[6] Esuli, A. and Sebastiani, F. Determining the semantic orientation of terms through gloss analysis. In Proceedings of CIKM-5, the ACM SIGIR Conference on Information and Knowledge Management, Bremen, DE. 2005
[7] Esuli, A. and Sebastiani, F. Determining term subjectivity and term orientation for opinion mining. In Proceedings ACL-06, the 11rd Conference of the European Chapter of the Association for Computational Linguistics, Trento, IT. (2006a).
[8] Esuli, A. and Sebastiani, F. SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining In Proceedings of LREC-06, 5th Conference on Language Resources and Evaluation, Genova, IT, 2006, pp. 417-422.  (2006b)
[9] W. Frawley and G. Piatetsky-Shapiro and C. Matheus (Fall 1992). "Knowledge Discovery in Databases: An Overview". AI Magazine: pp. 213-228
[10] Gamon, M., A. Aue. 2005. Automatic identification of sentiment vocabulary: exploiting low association with known sentiment terms. In Proceedings of the Workshop on Feature Engineering for Machine Learning in Natural Language Processing at ACL 2005., pages 57-64
[11] D. Hand, H. Mannila, P. Smyth (2001). Principles of Data Mining. MIT Press, Cambridge, MA
[12] Hatzivassiloglou, V. and Mckeown, K., 1997. Predicting the Semantic Orientation of Adjectives. In Proc. of 35th ACL/8th EACL.
[13] Hiroshi Kanayama, Tetsuya Nasukawa, & Hideo Watanabe: Deeper sentiment analysis using machine translation technology. Coling 2004: 20th International Conference on Computational Linguistics, 23-27 August 2004, University of Geneva, Switzerland, Proceedings; 7pp
[14] Jeonghee Yi, Wayne Niblack: Sentiment Mining in WebFountain. ICDE 2005: 1073-1083
[15] Kushal Dave, Steve Lawrence, David M. Pennock: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. WWW 2003: 519-528
[16] M. Baroni and S. Vegnaduzzo. 2004. Identifying subjective adjectives through web-based mutual information. In Ernst Buchberger (ed.), Proceedings of KONVENS 2004, Vienna: ÖGAI. 17-24.
[17] Minqing Hu, Bing Liu: Mining and summarizing customer reviews. KDD 2004: 168-177
[18] Minqing Hu, Bing Liu: Mining Opinion Features in Customer Reviews. To appear in AAAI’04, 2004.
[19] Randolph Quirk, Sidney Greenbaum, Geoffrey Leech, Jan Svartvik, "A Comprehensive Grammar of the English Language", Longman, 1985
[20] Shlomo Argamon, Kenneth Bloom, Andrea Esuliy, Fabrizio Sebastiani, "Automatically Determining Attitude Type and Force for Sentiment Analysis"
[21] Theresa Wilson, Janyce Wiebe, Paul Hoffmann: Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. HLT/EMNLP 2005
[22] Tony Mullen, Incorporating topic information into sentiment analysis models, 2004 ACL Poster Session, Barcelona
[23] Turney, P. D., Littman M. L., Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, 21(4):315–346. 2003
[24] Turney, P. D., Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL'02), Philadelphia, Pennsylvania, pp. 417-424. (NRC #44946) 2002

Opinion Mining을 보면 크게 
  1) Linguistic Resources를 구축하는 분야
  2) 응용 분야
     2-1) Polarity Classification
     2-2) Opinion Expression Extraction
으로나누어 지는데, 위의 논문들은 그냥 마구마구 정리된 것들입니다.
1) 에서는 [12],[8]을 추천하구요
2) 에서는 [17],[13],[5],[1]을 추천합니다.

출처 : http://tong.nate.com/purewaiting/44313140