Compositional rules of Korean auxiliary predicates for sentiment analysis
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초록
Most sentiment analysis systems count the number of occurrences of sentiment expressions in a text,and evaluate the text by summing polarity values of extracted sentiment expressions. However, linguistic contexts of the expressions should be taken into account in order to analyze sentimental orientation of the text meticulously. Korean auxiliary predicates affect meaning of the main verb or adjective in some ways while attached to it in their usage. In this paper, we introduce a new approach that handles Korean auxiliary predicates in the light of sentiment analysis. We classify the auxiliary predicates according to their strength of impact on sentiment polarity values. We also define compositional rules of auxiliary predicates to update polarity values when the predicates appear along with sentiment expressions. This approach is implemented to a sentiment analysis system to extract opinions about a specific individual from review documents which were collected from various web sites. An experimental result shows approximately 72.6% precision and 52.7% recall for correctly detecting sentiment expressions from a text.
Abstract
Most sentiment analysis systems count the number of occurrences of sentiment expressions in a text,and evaluate the text by summing polarity values of extracted sentiment expressions. However, linguistic contexts of the expressions should be taken into account in order to analyze sentimental orientation of the text meticulously. Korean auxiliary predicates affect meaning of the main verb or adjective in some ways while attached to it in their usage. In this paper, we introduce a new approach that handles Korean auxiliary predicates in the light of sentiment analysis. We classify the auxiliary predicates according to their strength of impact on sentiment polarity values. We also define compositional rules of auxiliary predicates to update polarity values when the predicates appear along with sentiment expressions. This approach is implemented to a sentiment analysis system to extract opinions about a specific individual from review documents which were collected from various web sites. An experimental result shows approximately 72.6% precision and 52.7% recall for correctly detecting sentiment expressions from a text.
참고문헌 (7)
- M. Taboada, "Lexicon-based methods for sentiment analysis", The Journal of Computational Linguists, 37(2), pp.267~307 (2010)
- 황순희, "인지동사의 의미분류와 어휘의미망 표상", 언어연구, 26(2), pp.373~406 (2010), 한국현대언어학회
- K. Moilanen, "Sentiment composition", Proceedings of the Recent Advances in Natural Language Processing International Conference (RANLP-2007), pp.378~382 (2007)
- Y. Choi, "Learning with compositional semantics as structural inference for subsentential sentiment analysis", Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)
- S. Chang, "Korean" (1996), John Benjamins Publishing Company
- Y. CHO, "Automatic affect recognition using natural language processing techniques and manually built affect lexicon", The Institute of Electronics, Information and Communication Engineers(IEICE) transactions of Information and Systems, 89(12), pp.2964~2971 (2006)
- R. Prasad, "The penn discourse treebank 2.0", Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08) (2008)