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A Heuristic Method for Extracting True Opinion Targets

의도된 의견 대상의 추출을 위한 경험적 방법

  • Soh, Yun-Kyu (Dept. of Computer Science and Engineering, Hanyang University) ;
  • Kim, Han-Woo (Dept. of Computer Science and Engineering, Hanyang University) ;
  • Jung, Sung-Hun (Dept. of Computer Science and Engineering, Hanyang University) ;
  • Kim, Dong-Ju (College of Liberal Arts, Anyang University)
  • Received : 2012.09.11
  • Accepted : 2012.09.18
  • Published : 2012.09.30

Abstract

The opinion of user on a certain product is expressed in positive/negative sentiments for specific features of it. In some cases, they are expressed for a holistic part of homogeneous specific features, or expressed for product itself. Therefore, in the area of opinion mining, name of opinion features to be extracted are specific feature names, holonyms for theses specific features, and product names. However, when the opinion target is described with product name or holonym, sometimes it may not match feature name of opinion sentence to true opinion target intended by the reviewer. In this paper, we present a method to extract opinion targets from opinion sentences. Most importantly, we propose a method to extract true target from the feature names mismatched to a intended target. First, we extract candidate opinion pairs using dependency relation between words, and then select feature names frequently mismatched to opinion target. Each selected opinion feature name is replaced to a specific feature intended by the reviewer. Finally, in order to extract relevant opinion features from the whole candidate opinion pairs including modified opinion feature names, candidate opinion pairs are rearranged by the order of user's interest.

일반적으로 사람들은 특정 상품에 관한 의견을 표현할 때 그 상품이 갖는 개별속성에 대해 긍부정 성향을 표시한다. 어떤 경우에는 상품이 갖는 동질의 개별 속성에 대해 포괄적으로 긍부정 성향을 표현하거나 상품 자체에 대해 표현하기도 한다. 따라서 의견검색 분야에서 추출 대상이 되는 의견 속성명에는 상품의 개별 속성명, 이 개별 속성들을 포함하는 전체어, 그리고 상품명이 존재한다. 그러나 의견 대상을 상품명이나 전체어로 표현할 때, 경우에 따라 의견문장 표면에 나타나는 속성명과 의견 작성자가 의도한 실제 대상이 일치하지 않을 수도 있다. 본 논문에서는 의견문장으로부터 의견 대상을 추출하는 방법을 제시한다. 무엇보다 우리는 의도한 대상과 일치하지 않는 속성명으로부터 의도한 대상을 추출하기 위한 새로운 방법을 제안한다. 제시하는 방법에서는 단어간 의존관계를 이용하여 의견속성 후보쌍을 추출하고, 추출된 후보쌍들 중 의견 대상과 일반적으로 빈번히 불일치하는 속성명을 선택한다. 선택된 속성명을 작성자가 의도한 개별속성으로 변경한 뒤, 이를 포함한 전체 의견속성 후보쌍들로부터 적합한 의견속성을 추출하기 위해 사람들이 관심 있어할만한 순으로 재배열하게 된다.

Keywords

References

  1. Hu. M, and Liu. B, "Mining and summarizing customer reviews," In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, Seattle, WA, USA, pp. 168-177, 2004.
  2. Hu. M, and Liu. B, "Mining opinion features in customer reviews," In Proceedings of American Association for Artificial Intelligence, pp. 755-760, 2004.
  3. A. Qadir, "Detecting opinion sentences specific to product features in customer reviews using typed dependency relations," eETTs Proceeding of the Workshop on Events in Emerging Text Types, Singapore, pp. 38-43, 2009.
  4. G. Qiu, B. Liu, J. Bu, C. Chen, "Opinion word expansion and target extraction through double propagation," In Proceedings of the ACL, pp. 9-27, 2011.
  5. Mahesh. Joshi, and Carolyn. Penstein-Rose, "Generalizing dependency features for opinion mining," In Proceedings of the ACL-IJCNLP 2009 Conference Short Paper," Suntec, Singapore, pp. 313-316, 2009.
  6. Ellen. Riloff, Siddharth. Patwardhan, and Janyce. Wiebe, "Feature subsumption for opinion analysis," In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Sydney, pp. 440-448, 2006.
  7. A. Esuli, and F. Sebastiani, "SentiWordNet: A publicly available lexical resource for opinion mining," In Proceedings of LREC-06, the 5th Conference on Language Resources and Evaluation, Genova, IT, pp. 417-422, 2006.
  8. N. Jakob, I. Gurevych, "w," In Proceedings of the ACL 2010 conference short papers, pp. 263-268, 2010.
  9. AM. Popescu, and O. Etzioni, "Extracting product features and opinions from reviews," In Proceedings of Human Language Technology Conference on Emprirical Methods in Natural Language Processing, Vancouver, pp. 339-346, 2005.
  10. Marie-Catherine Marneffe, and Christopher D. Manning, "Stanford typed dependencies manual," Stanford Parser, 2011.