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http://dx.doi.org/10.5916/jkosme.2010.34.6.871

Feature Weighting for Opinion Classification of Comments on News Articles  

Lee, Kong-Joo (충남대학교 정보통신공학과)
Kim, Jae-Hoon (한국해양대학교 컴퓨터공학과)
Seo, Hyung-Won (한국해양대학교 컴퓨터공학과)
Rhyu, Keel-Soo (한국해양대학교 컴퓨터공학과)
Publication Information
Journal of Advanced Marine Engineering and Technology / v.34, no.6, 2010 , pp. 871-879 More about this Journal
Abstract
In this paper, we present a system that classifies comments on a news article into a user opinion called a polarity (positive or negative). The system is a kind of document classification system for comments and is based on machine learning techniques like support vector machine. Unlike normal documents, comments have their body that can influence classifying their opinions as polarities. In this paper, we propose a feature weighting scheme using such characteristics of comments and several resources for opinion classification. Through our experiments, the weighting scheme have turned out to be useful for opinion classification in comments on Korean news articles. Also Korean character n-grams (bigram or trigram) have been revealed to be helpful for opinion classification in comments including lots of Internet words or typos. In the future, we will apply this scheme to opinion analysis of comments of product reviews as well as news articles.
Keywords
Opinion classification; News comments; Sentiment lexicon; Feature weighting;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 홍진표, 차정원, "TextRank 알고리즘을 이용한 한국어 중요 문장 추출", 한국정보과학회 2009 한국컴퓨터종합학술대회 발표논문집, 제36권, 제1호(C), pp. 311-314, 2009.
2 J.-H. Kim, K. J. Lee, H.-W. Seo, and H.-C. Kim, "Opinion mining for comments on news articles on the Web", Proceedings of the International Conference on Internet, pp. 63-68, 2009.
3 Z. Zheng, X. Wu, and R. Srihari, "Feature selection for text categorization on imbalanced data", ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 80-89, 2004.   DOI
4 D. Rao and D. Ravichandran, "Semi-supervised polarity lexicon induction", Proceedings of the 12th Conference of the European Chapter of the ACL, pp. 675-682, 2009.
5 C. Kim and Y. Kim, "Statistical information of Korean dictionary to construct an enormous electronic dictionary", The Journal of Korean Contents Society, vol. 7, no. 6, pp. 60-68, 2007.   과학기술학회마을   DOI
6 J. Lee, H. Park, J. Ahn and M. Kim, "An effective indexing methods for Korean text", Proceedings of the Korean Society for Information Management Conference, pp. 11-14, 1995.
7 C. Jung, An Indexing Method Based on the Mixed n-gram for Korean Information Retrieval, Master Thesis, Department of Computer Engineering, Korea Maritime University, 2004.
8 A. Devitt and K. Ahmad, "Sentiment polarity identification in financial news: A cohesion-based approach", Proceedings of the Annual Meeting of the Association of Computational Linguistics, pp. 984-991, 2007.
9 C. M. Tan and C. D. Lee, "The use of bigram to enhance text categorization", International Journal of Information Processing & Management, pp. 529-546, 2001.
10 A. Nourbakhsh, C. Khoo, and J.-C. Na, "A framework for sentiment analysis of political news articles", Proceedings of the International Communication Association Conference, 2008.
11 T. Bekkerman and J. Allan, Using Bigrams in Text Categorization, CIIT Technical Report IR-408, 2004.
12 B. Pang and L. Lee, "Opinion mining and sentiment analysis", Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.   DOI
13 K. Lin, C. Yang and H.-H. Chen, "What emotions do news articles trigger in their readers?", Proceedings of SIGIR, pp. 733-734, 2007.
14 R. Tong, "An operational system for detecting and tracking opinions in on-line discussions", Working Notes of the SIGIR Workshop on Operational Text Classification, pp. 1-6, 2001.
15 E. Spertus, "Somkey: Automatic recognition of hostile messages", Proceedings of the 5th International Conference on Intelligent User Interfaces, pp. 1058-1065, 1997.
16 P. Turney, "Thumbs Up or thumbs down? Semantic orientation applied to unsupervised classification of reviews", Proceedings of ACL, pp. 417-424, 2002.
17 T. Wilson, J. Wiebe and P. Hoffmann, "Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis", Computational Linguistics, vol. 35, no. 3, pp. 399-433, 2009.   DOI
18 N. Godbole, M. Srinivasaiah, and S. Skiena, "Large-Scale Sentiment Analysis for News and Blogs", Proceedings of International Conference on Weblogs and Social Media, 2007.
19 C. D. Manning, P. Raghavan and H. Schutze, Introduction to Information Retrieval, Cambridge University Press. 2008.
20 A. Zheng and R Srihari, "Optimally combining positive and negative features for text categorization", Proceedings of the ICML Workshop on Learning from Imbalanced Datasets, 2003.