A Graph-Based Approach for Sentiment Sentence Extraction

  • Kazutaka Shimada
  • Daigo Hashimoto
  • Tsutomu Endo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5433)


As the World Wide Web rapidly grows, a huge number of online documents are easily accessible on the Web. We obtain a huge number of review documents that include user’s opinions for products. To classify the opinions is one of the hottest topics in natural language processing. In general, we need a large amount of training data for the classification process. However, construction of training data by hand is costly. In this paper, we examine a method of sentiment sentence extraction. This task is to classify sentences in documents into opinions and non-opinions. For the task, we use the Hierarchical Directed Acyclic Graph (HDAG) proposed by Suzuki et al. We obtained high accuracy in the sentiment sentence extraction task. The experimental result shows the effectiveness of the method based on the HDAG.


Sentiment Analysis Sentiment Sentence Extraction Graph-based Approach Hierarchical Directed Acyclic Graph Similarity 


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  1. 1.
    Cancedda, N., Gaussier, E., Goutte, C., Renders, J.M.: Word-sequence kernels. J. Machine Learning Research 3, 1059–1082 (2003)Google Scholar
  2. 2.
    Collins, M., Duffy, N.: Convolution kernels for natural language. In: Advances in Neural Information Processing Systems, vol. 14 (2002)Google Scholar
  3. 3.
    Hirao, T., Suzuki, J., Isozaki, H., Maeda, E.: Dependency-based sentence alignment for multiple document summarization. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004) (2004)Google Scholar
  4. 4.
    Ikehara, S., Miyazaki, M., Shirai, S., Yokoo, A., Nakaiwa, H., Ogura, K., Ooyama, Y., Hayashi, Y. (eds.): Goi-Taikei. A Japanese Lexicon (in Japanese). Iwanami Shoten (1997)Google Scholar
  5. 5.
    Kaji, N., Kitsuregawa, M.: Automatic construction of polarity-tagged corpus from html documents. In: Proceedings of the 21st International Conference on Computational Linguistics (COLING/ACL 2006), pp. 452–459 (2006)Google Scholar
  6. 6.
    Kaji, N., Kitsuregawa, M.: Building lexicon for sentiment analysis from massive html documents. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-CoNLL 2007) (2007)Google Scholar
  7. 7.
    Kawaguchi, T., Matsui, T., Ohwada, H.: Opinion extraction from weblog using svm and newspaper article (in Japanese). In: The 20th Annual Conference of the Japanese Society for Artificial Intelligence (2006)Google Scholar
  8. 8.
    Kobayashi, N., Iida, R., Inui, K., Matsumoto, Y.: Opinion extraction using a learning-based anaphora resolution technique. In: Proceedings of the Second International Joint Conference on Natural Language Processing (IJCNLP 2005), pp. 175–180 (2005)Google Scholar
  9. 9.
    Kudo, T., Matsumoto, Y.: A boosting algorithm for classification of semi-structured text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2004)Google Scholar
  10. 10.
    Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text classification using string kernel. J. Machine Learning Research 2, 419–444 (2002)Google Scholar
  11. 11.
    Osajima, I., Shimada, K., Endo, T.: Classification of evaluative sentences using sequential patterns. In: Proceedings of the 11nd Annual Meeting of The Association for Natural Language Processing (in Japanese) (2005)Google Scholar
  12. 12.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)Google Scholar
  13. 13.
    Suzuki, J., Sasaki, Y., Maeda, E.: Hierarchical directed acyclic graph kernel. Systems and Computers in Japan 37(10), 58–68 (2006)CrossRefGoogle Scholar
  14. 14.
    Takamura, H., Inui, T., Okumura, M.: Extracting semantic orientations of words using spin model. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 133–140 (2005)Google Scholar
  15. 15.
    Touge, Y., Ohashi, K., Yamamoto, K.: Extracting opinion sentence adapted to topic using iteration learning (in Japanese). In: IPSJ SIG Notes, pp. 43–50 (2004)Google Scholar
  16. 16.
    Tsutsumi, K., Shimada, K., Endo, T.: Movie review classification based on a multiple classifier. In: the 21th Pacific Asia Conference on Language, Information and Computation (PACLIC) (2007)Google Scholar
  17. 17.
    Turney, P.D.: Thumbs up? or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 417–424 (2002)Google Scholar
  18. 18.
    Wiebe, J., Riloff, E.: Creating subjective and objective sentence classifiers from unannotated texts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 486–497. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kazutaka Shimada
    • 1
  • Daigo Hashimoto
    • 1
  • Tsutomu Endo
    • 1
  1. 1.Department of Artificial IntelligenceKyushu Institute of TechnologyFukuokaJapan

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