Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Text Processing and Computational Linguistics

CICLing 2011: Computational Linguistics and Intelligent Text Processing pp 380–392Cite as

Towards Well-Grounded Phrase-Level Polarity Analysis

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. 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. 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. 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. 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. Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)

    Article  Google Scholar 

  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. Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning Subjective Language. Computational Linguistics 30(3), 277–308 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Moilanen, K., Pulman, S.: Sentiment Composition. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing, RANLP (2007)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  15. Lyons, J.: Semantics, vol. 2. Cambridge University Press, Cambridge (1977)

    Book  Google Scholar 

  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)

    Chapter  Google Scholar 

  17. Das, S., Chen, M.: Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web. Management Science 53(9), 1375–1388 (2007)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  19. Remus, R., Hänig, C.: Software and data set accompanying this work (2011), www.CICLing.org/2011/software/70

  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. 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. 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. 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. Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20, 37–46 (1960)

    Article  Google Scholar 

  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. Landis, J., Koch, G.: The Measurement of Observer Agreement for Categorical Data. Biometrics 33(1), 159–174 (1977)

    Article  MATH  Google Scholar 

  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. 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 

Download references

Author information

Authors and Affiliations

  1. Natural Language Processing Group Department of Computer Science, University of Leipzig, 04103, Leipzig, Germany

    Robert Remus & Christian Hänig

Authors
  1. Robert Remus
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian Hänig
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, Mexico

    Alexander F. Gelbukh

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Remus, R., Hänig, C. (2011). Towards Well-Grounded Phrase-Level Polarity Analysis. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19400-9_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19399-6

  • Online ISBN: 978-3-642-19400-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation