Skip to main content
SpringerLink
Log in
Book cover

European Conference on Information Retrieval

ECIR 2013: Advances in Information Retrieval pp 730–733Cite as

Topic Model for User Reviews with Adaptive Windows

  • Conference paper

  • 2957 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7814))

Abstract

We discuss the problem in applying topic models to user reviews. Different from ordinary documents, reviews in a same category are similar to each other. This makes it difficult to estimate meaningful topics from these reviews. In this paper, we develop a new model for this problem using the distance dependent Chinese restaurant process. It need not decide the size of windows and can consider neighboring sentences adaptively. We compare this model to the Multi-grain latent Dirichlet allocation which has been proposed previously, and show that our model achieves better results in terms of perplexity.

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   99.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
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. Blei, D.M., Ng, A., Jordan, M.I.: Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)

    MATH  Google Scholar 

  2. Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International World Wide Web Conference, pp. 112–120 (2008)

    Google Scholar 

  3. Blei, D.M., Frazier, P.: Distance dependent Chinese restaurant processes. Journal of Machine Learning Research 12, 2461–2488 (2011)

    MathSciNet  Google Scholar 

  4. Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Sciences 101, 5228–5235 (2004)

    Article  Google Scholar 

  5. Minka, T.P.: Estimating a Dirichlet distribution. Technical report, Microsoft (2000)

    Google Scholar 

  6. Ritter, T., Tanner, M.: Facilitating the Gbibs sampler: The Gbibs stopper and the Griddy-Gbibs sampler. Journal of the American Statistical Association 87, 861–868 (1992)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, Japan

    Takuya Konishi

  2. Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, Japan

    Fuminori Kimura & Akira Maeda

Authors
  1. Takuya Konishi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fuminori Kimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Akira Maeda
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Yandex, Leo Tolstoy, 16, 119021, Moscow, Russia

    Pavel Serdyukov  & Ilya Segalovich  & 

  2. Kontur Labs and Ural Federal University, Fonvizina 3-27, 620078, Yekaterinburg, Russia

    Pavel Braslavski

  3. National Research University Higher School of Economics (HSE), Pokrovskii bd 11, 109028, Moscow, Russia

    Sergei O. Kuznetsov

  4. University of Amsterdam, Turfdraagsterpad 9, 1012 XT, Amsterdam, The Netherlands

    Jaap Kamps

  5. Knowledge Media Institute, The Open University, Walton Hall, MK7 6AA, Milton Keynes, UK

    Stefan Rüger

  6. Mathematics & Computer Science Department, Emory University, 400 dowman Drive, 30329, Atlanta, GA, USA

    Eugene Agichtein

  7. Department of Computer Science, University College London, Gower Street, WC1E 6BT, London, UK

    Emine Yilmaz

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Konishi, T., Kimura, F., Maeda, A. (2013). Topic Model for User Reviews with Adaptive Windows. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_71

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-36973-5_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36972-8

  • Online ISBN: 978-3-642-36973-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation