Skip to main content

Opinion Mining by Transformation-Based Domain Adaptation

  • Conference paper
Text, Speech and Dialogue (TSD 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6231))

Included in the following conference series:

  • 1473 Accesses

Abstract

Here we propose a novel approach for the task of domain adaptation for Natural Language Processing. Our approach captures relations between the source and target domains by applying a model transformation mechanism which can be learnt by using labeled data of limited size taken from the target domain. Experimental results on several Opinion Mining datasets show that our approach significantly outperforms baselines and published systems when the amount of labeled data is extremely small.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Daumé III, H., Marcu, D.: Domain adaptation for statistical classifiers. J. Artif. Int. Res. 26, 101–126 (2006)

    MATH  Google Scholar 

  2. Kobayashi, N., Inui, K., Matsumoto, Y.: Extracting Aspect-Evaluation and Aspect-of Relations in Opinion Mining. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 1065–1074 (2007)

    Google Scholar 

  3. Joachims, T.: Making Large-Scale Support Vector Machine Learning Practical, pp. 169–184 (1999)

    Google Scholar 

  4. Chelba, C., Acero, A.: Adaptation of Maximum Entropy Capitalizer: Little Data Can Help a lot. Computer Speech & Language 20, 382–399 (2006)

    Article  Google Scholar 

  5. Daumé III, H.: Frustratingly Easy Domain Adaptation. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 256–263 (2007)

    Google Scholar 

  6. Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of Representations for Domain Adaptation. In: Advances in Neural Information Processing Systems, vol. 20, MIT Press, Cambridge (2007)

    Google Scholar 

  7. Gupta, R., Sarawagi, S.: Domain Adaptation of Information Extraction Models. SIGMOD Rec. 37, 35–40 (2008)

    Article  Google Scholar 

  8. Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain Adaptation via Transfer Component Analysis. In: IJCAI, pp. 1187–1192 (2009)

    Google Scholar 

  9. Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain Adaptation with Multiple Sources. In: NIPS, pp. 1041–1048 (2008)

    Google Scholar 

  10. Snyman, J.A.: Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms (Applied Optimization). Springer, New York (2005)

    MATH  Google Scholar 

  11. Blitzer, J., Dredze, M., Pereira, F.: Biographies, Bollywood, Boom-Boxes and Blenders: Domain Adaptation for Sentiment Classification. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 440–447 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ormándi, R., Hegedűs, I., Farkas, R. (2010). Opinion Mining by Transformation-Based Domain Adaptation. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2010. Lecture Notes in Computer Science(), vol 6231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15760-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15760-8_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15759-2

  • Online ISBN: 978-3-642-15760-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics