Skip to main content

Learning Higher-Level Features with Convolutional Restricted Boltzmann Machines for Sentiment Analysis

  • Conference paper
Advances in Information Retrieval (ECIR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9022))

Included in the following conference series:

Abstract

In recent years, learning word vector representations has attracted much interest in Natural Language Processing. Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics. In this paper we go beyond the vector representations at the word level and propose a novel framework that learns higher-level feature representations of n-grams, phrases and sentences using a deep neural network built from stacked Convolutional Restricted Boltzmann Machines (CRBMs). These representations have been shown to map syntactically and semantically related n-grams to closeby locations in the hidden feature space. We have experimented to additionally incorporate these higher-level features into supervised classifier training for two sentiment analysis tasks: subjectivity classification and sentiment classification. Our results have demonstrated the success of our proposed framework with 4% improvement in accuracy observed for subjectivity classification and improved the results achieved for sentiment classification over models trained without our higher level features.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /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
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

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. Collobert, R., Weston, J.: A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. In: ICML (2008)

    Google Scholar 

  2. Dahl, G.E., Adams, R.P., Larochelle, H.: Training Restricted Boltzmann Machines on Word Observations. In: ICML (2012)

    Google Scholar 

  3. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional Neural Network for Modelling Sentences. In: ACL (2014)

    Google Scholar 

  4. Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. In: ICML (2009)

    Google Scholar 

  5. Lin, C., He, Y., Everson, R.: Sentence Subjectivity Detection with Weakly-Supervised Learning. In: IJCNLP (2011)

    Google Scholar 

  6. Mikolov, T., Zweig, G.: Context Dependent Recurrent Neural Networkl Language Model. Tech. rep., Microsoft Research Technical Report (2012)

    Google Scholar 

  7. Nakagawa, T., Inui, K., Kurohashi, S.: Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables. In: NAACL (2010)

    Google Scholar 

  8. Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2(12), 1–135 (2008)

    Article  Google Scholar 

  9. Socher, R., Pennington, J., Huang, E.H., Ng, A.Y., Manning, C.D.: Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions. In: EMNLP (2011)

    Google Scholar 

  10. Wiebe, J., Riloff, E.: Creating Subjective and Objective Sentence Classifiers from Unannotated Texts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 486–497. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Huynh, T., He, Y., Rüger, S. (2015). Learning Higher-Level Features with Convolutional Restricted Boltzmann Machines for Sentiment Analysis. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16354-3_49

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16353-6

  • Online ISBN: 978-3-319-16354-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics