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Towards Well-Grounded Phrase-Level Polarity Analysis

  • Robert Remus
  • Christian Hänig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6608)

Abstract

We propose a new rule-based system for phrase-level polarity analysis and show how it benefits from empirically validating its polarity composition through surveys with human subjects. The system’s two-layer architecture and its underlying structure, i.e. its composition model, are presented. Two functions for polarity aggregation are introduced that operate on newly defined semantic categories. These categories detach a word’s syntactic from its semantic behavior. An experimental setup is described that we use to carry out a thorough evaluation. It incorporates a newly created German-language data set that is made freely and publicly available. This data set contains polarity annotations at word-level, phrase-level and sentence-level and facilitates comparability between different studies and reproducibility of our results.

Keywords

Word Pair Polarity Analysis Word Form Sentiment Analysis Syntactic Constituent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Hatzivassiloglou, V., McKeown, K.: Predicting the Semantic Orientation of Adjectives. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 174–181 (1997)Google Scholar
  2. 2.
    Kim, S., Hovy, E.: Determining the Sentiment of Opinions. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING), pp. 1367–1373 (2004)Google Scholar
  3. 3.
    Kim, S., Hovy, E.: Automatic Detection of Opinion Bearing Words and Sentences. In: Proceedings of the 2nd International Joint Conference on Natural Language Processing, IJCNLP (2005)Google Scholar
  4. 4.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification using Machine Learning Techniques. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 79–86 (2002)Google Scholar
  5. 5.
    Hänig, C., Schierle, M.: Relation Extraction based on Unsupervised Syntactic Parsing. In: Proceedings of the Conference on Text Mining Services, pp. 65–70. University of Leipzig, Leipzig (2009)Google Scholar
  6. 6.
    Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  7. 7.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing Contextual Polarity in Phrase-level Sentiment Analysis. In: Proceedings of the Conference on Human Language Technology (HLT) and Empirical Methods in Natural Language Processing (EMNLP), pp. 347–354 (2005)Google Scholar
  8. 8.
    Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning Subjective Language. Computational Linguistics 30(3), 277–308 (2004)CrossRefGoogle Scholar
  9. 9.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing Contextual Polarity: an Exploration of Features for Phrase-level Sentiment Analysis. Computational Linguistics 35(3), 399–433 (2009)CrossRefGoogle Scholar
  10. 10.
    Agarwal, A., Biadsy, F., Mckeown, K.: Contextual Phrase-level Polarity Analysis Using Lexical Affect Scoring and Syntactic N-grams. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pp. 24–32 (2009)Google Scholar
  11. 11.
    Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Compositionality Principle in Recognition of Fine-grained Emotions from Text. In: Proceedings of the 3rd International Conference on Weblogs and Social Media (ICWSM), pp. 278–281 (2009)Google Scholar
  12. 12.
    Moilanen, K., Pulman, S.: Sentiment Composition. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing, RANLP (2007)Google Scholar
  13. 13.
    Remus, R., Quasthoff, U., Heyer, G.: SentiWS – a Publicly Available German-language Resource for Sentiment Analysis. In: Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC), pp. 1168–1171 (2010)Google Scholar
  14. 14.
    Wilks, Y., Stevenson, M.: The Grammar of Sense: Using Part-of-Speech Tags as a First Step in Semantic Disambiguation. Natural Language Engineering 4(2), 135–143 (1998)CrossRefGoogle Scholar
  15. 15.
    Lyons, J.: Semantics, vol. 2. Cambridge University Press, Cambridge (1977)CrossRefGoogle Scholar
  16. 16.
    Polanyi, L., Zaenen, A.: Contextual Valence Shifters. In: Shanahan, J., Qu, Y., Wiebe, J. (eds.) Computing Attitude and Affect in Text: Theory and Application. The Information Retrieval Series, vol. 20, pp. 1–9. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Das, S., Chen, M.: Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web. Management Science 53(9), 1375–1388 (2007)CrossRefGoogle Scholar
  18. 18.
    Cacioppo, J., Larsen, J., Smith, N., Berntson, G.: What Lurks below the Surface of Feelings? In: Manstead, A., Frijda, N., Fischer, A. (eds.) Feelings and Emotions: the Amsterdam Symposium, pp. 223–242. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  19. 19.
    Remus, R., Hänig, C.: Software and data set accompanying this work (2011), www.CICLing.org/2011/software/70
  20. 20.
    Toutanova, K., Manning, C.: Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-speech Tagger. In: Proceedings of Joint Conference on Empirical Methods in Natural Language Processing (EMNLP) and Very Large Corpora (VLC), pp. 63–71 (2000)Google Scholar
  21. 21.
    Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-rich Part-of-speech Tagging with a Cyclic Dependency Network. In: Proceedings of the Human Language Technologies: North American Chapter of the Association for Computational Linguistics (HLT-NAACL), pp. 173–180 (2003)Google Scholar
  22. 22.
    Klein, D., Manning, C.: Accurate Unlexicalized Parsing. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL), pp. 423–430 (2003)Google Scholar
  23. 23.
    Klein, D., Manning, C.: Fast Exact Inference with a Factored Model for Natural Language Parsing. Advances in Neural Information Processing Systems 15, 3–10 (2003)Google Scholar
  24. 24.
    Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20, 37–46 (1960)CrossRefGoogle Scholar
  25. 25.
    Randolph, J.: Free-Marginal Multirater Kappa (multirater κfree): An Alternative to Fleiss Fixed-Marginal Multirater Kappa. Presented at the Joensuu Learning and Instruction Symposium (2005)Google Scholar
  26. 26.
    Landis, J., Koch, G.: The Measurement of Observer Agreement for Categorical Data. Biometrics 33(1), 159–174 (1977)CrossRefzbMATHGoogle Scholar
  27. 27.
    Tsur, O., Davidov, D., Rappoport, A.: ICWSM – A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews. In: Proceedings of the Forth International AAAI Conference on Weblogs and Social Media, Washington D.C., USA, pp. 162–169 (2010)Google Scholar
  28. 28.
    McDonald, R., Hannan, K., Neylon, T., Wells, M., Reynar, J.: Structured Models for Fine-to-Coarse Sentiment Analysis. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 432–439 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Robert Remus
    • 1
  • Christian Hänig
    • 1
  1. 1.Natural Language Processing Group Department of Computer ScienceUniversity of LeipzigLeipzigGermany

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