Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-16T10:40:11.642Z Has data issue: false hasContentIssue false
  • English
  • Français

Unsupervised lexicon induction for clause-level detection of evaluations

Published online by Cambridge University Press:  30 March 2011

HIROSHI KANAYAMA  and
TETSUYA NASUKAWA
HIROSHI KANAYAMA
Affiliation:
IBM Research – Tokyo, 1623-14 Shimotsuruma, Yamato-shi, Kanagawa-ken 242-8502, Japan e-mail: hkana@jp.ibm.com, nasukawa@jp.ibm.com
TETSUYA NASUKAWA
Affiliation:
IBM Research – Tokyo, 1623-14 Shimotsuruma, Yamato-shi, Kanagawa-ken 242-8502, Japan e-mail: hkana@jp.ibm.com, nasukawa@jp.ibm.com
Get access Rights & Permissions [Opens in a new window]

Abstract

This article proposes clause-level evaluation detection, which is a fine-grained type of opinion mining, and describes an unsupervised lexicon building method for capturing domain-specific knowledge by leveraging the similar polarities of sentiments between adjacent clauses. The lexical entries to be acquired are called polar atoms, the minimum human-understandable syntactic structures that specify the polarity of clauses. As a hint to obtain candidate polar atoms, we use context coherency, the tendency for the same polarity to appear successively in a context. Using the overall density and precision of coherency in the corpus, the statistical estimation picks up appropriate polar atoms from among the candidates, without any manual tuning of the threshold values. The experimental results show that the precision of polarity assignment with the automatically acquired lexicon was 83 per cent on average, and our method is robust for corpora in diverse domains and for the size of the initial lexicon.

Type
Articles
Information
Natural Language Engineering , Volume 18 , Issue 1 , January 2012 , pp. 83 - 107
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahmad, K., Taskaya–Temizel, T., Cheng, D., Gillam, L., Ahmad, S., Traboulsi, H., and Nankervis, J. 2004. Financial information grid – an ESRC e-Social science pilot. In Proceedings of the UK e-Science all Hands Meeting, Edinburgh, UK.Google Scholar
Blyth, C. R. 1986. Approximate binomial confidence limits. Journal of the American Statistical Association 81 (395): 843–55.Google Scholar
Dave, K., Lawrence, S. and Pennock, D. M. 2003. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International Conference on World Wide Web, Budapest, Hungary, pp. 519–28.CrossRefGoogle Scholar
Du, W. and Tan, S. 2009. An iterative reinforcement approach for fine-grained opinion mining. In Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics, Boulder, CO, pp. 486–92.Google Scholar
Gamon, M. 2004. Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In Proceedings of the 20th International Conference on Computational Linguistics (COLING), Geneva, Switzerland.Google Scholar
Goldberg, A. B. and Zhu, J. 2006. Seeing stars when there aren't many stars: Graph-based semi-supervised learning for sentiment categorization. In Proceedings of TextGraphs: HLT/NAACL Workshop on Graph-Based Algorithms for Natural Language Processing, New York.Google Scholar
Hatzivassiloglou, V. and McKeown, K. R. 1997. Predicting the semantic orientation of adjectives. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and the 8th European Chapter of the Annual Meeting of the Association for Computational Linguistics (ACL-EACL), Madrid, Spain, pp. 174–81.Google Scholar
Higashinaka, R., Prasad, R. and Walker, M. A. 2006. Learning to generate naturalistic utterances using reviews in spoken dialogue systems. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, pp. 265–72.Google Scholar
Hu, M. and Liu, B. 2004. Mining and summarizing customer reviews. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Seattle, Washington, pp. 168–77.Google Scholar
Kaji, N. and Kitsuregawa, M. 2007. Building lexicon for sentiment analysis from massive collection of HTML documents. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Prague, Czech Republic, pp. 1075–83.Google Scholar
Kanayama, H., Nasukawa, T. and Watanabe, H. 2004. Deeper sentiment analysis using machine translation technology. In Proceedings of the 20th International Conference on Computational Linguistics (COLING), Geneva, Switzerland, pp. 494–500.Google Scholar
Kanayama, H., Torisawa, K., Mitsuishi, Y. and Tsujii, J. 2000. A hybrid Japanese parser with hand-crafted grammar and statistics. In Proceedings of the 18th International Conference on Computational Linguistics (COLING), Saarbrücken, Germany, pp. 411–17.CrossRefGoogle Scholar
Meena, A. and Prabhakar, T. V. 2007. Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis. In Proceedings of the 29th European Conference on IR Research (ECIR), Rome, Italy, pp. 573–80.Google Scholar
Nasukawa, T. and Yi, J. (2003) Sentiment analysis: capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture, Sanibel, FL, pp. 70–7.CrossRefGoogle Scholar
Nigam, K. and Hurst, M. 2004. Towards a robust metric of opinion. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: theories and Applications, Stanford, CA.Google Scholar
Pang, B. and Lee, L. 2004. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, pp. 271–8.Google Scholar
Pang, B. and Lee, L. 2005. Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, Ann Arbor, MI, pp. 115–24.Google Scholar
Pang, B. and Lee, L. 2008. Opinion mining and sentiment analysis. Foundation and Trends in Information Retrieval 2 (1–2): 1–135.CrossRefGoogle Scholar
Pang, B., Lee, L. and Vaithyanathan, S. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of Empirical Methods in Natural Language Processing (EMNLP 2002), Philadelphia, PA, pp. 79–86.Google Scholar
Popescu, A.-M. and Etzioni, O. 2005. Extracting product features and opinions from reviews. In Proceedings of the Joint Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), Vancouver, British Columbia, Canada, pp. 339–46.Google Scholar
Qiu, G., Liu, B., Bu, J. and Chen, C. 2009. Expanding domain sentiment lexicon through double propagation. In Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, CA, pp. 1199–204.Google Scholar
Riloff, E. and Wiebe, J. 2003. Learning extraction patterns for subjective expressions. In Proceedings of Empirical Methods in Natural Language Processing (EMNLP 2003), Sapporo, Japan, pp. 105–12.Google Scholar
Turney, P. D. 2002. 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, Philadelphia, PA, pp. 417–24.Google Scholar
Wiebe, J. M., Wilson, T., Bruce, R., Bell, M., and Martin, M. 2004. Learning subjective language. Computational Linguistics 30 (3): 277–308.CrossRefGoogle Scholar
Wilson, T., Wiebe, J. and Hoffmann, P. (2009) Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Computational Linguistics, 35 (3): 399–433.CrossRefGoogle Scholar
Wilson, T., Wiebe, J. and Hwa, R. 2006. Recognizing strong and weak opinion clauses. Computational Intelligence 2 (2): 73–99.CrossRefGoogle Scholar
Wright, A. 2009. Our sentiments, exactly. Communications of the ACM 52: 14–15.Google Scholar
Yi, J., Nasukawa, T., Bunescu, R. and Niblack, W. 2003. Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In Proceedings of the Third IEEE International Conference on Data Mining, Melbourne, FL, pp. 427–34.CrossRefGoogle Scholar
Yu, H. and Hatzivassiloglou, V. 2003. Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of Empirical Methods in Natural Language Processing (EMNLP 2003), Sapporo, Japan, pp. 129–36.Google Scholar