Skip to main content

Opinion Classification in Conversational Content Using N-grams

  • Conference paper

Part of the book series: Studies in Computational Intelligence ((SCI,volume 513))

Abstract

The paper introduces the problem of opinion classification related to conversational content. It describes briefly various approaches known in this field. The focus is on a novelty method which has been designed on the basis of cyclic usage of n-grams (4-grams). This method belongs to lexicon based approaches. The contribution describes implementation of this method for the Slovak language, test results of the presented implementation and discussion of the achieved results as well.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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. Choi, Y., Cardie, C.: Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis. In: Proc. of the EMNLP 2008, Conference on Empirical Methods in Natural Language Processing, pp. 793–801 (2008)

    Google Scholar 

  2. Go, A.: Twitter Sentiment Classification using Distant Supervision. Stanford University, http://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf (cit. June 4, 2013)

  3. Levallois, C.: Umigon: sentiment analysis for tweets based on lexicons and heuristics, pp. 1–4. Erasmus University Rotterdam, The Netherlands (2013)

    Google Scholar 

  4. Liu, B.: Sentiment Analysis and Opinion Mining (Introduction and Survey), pp. 1–168. Morgan & Claypool Publisher (May 2012)

    Google Scholar 

  5. Lukáč, G., Butka, P., Mach, M.: Semantically-enhanced Extension of the Discussion Analysis Algorithm in SAKE. In: SAMI 2008, 6th International Symposium on Applied Machine Intelligence and Informatics, Herľany, Slovakia, pp. 241–246 (January 2008)

    Google Scholar 

  6. Mach, M., Lukáč, G.: A Dedicated Information Collection as an Interface to Newsgroup Discussions. In: IIS 2007 - 18th International Conference on Information and Intelligent Systems, Varazdin, Croatia, September 12-14, pp. 163–169 (2007) ISBN 978-953-6071-30-2

    Google Scholar 

  7. Smatana, M., Koncz, P., Paralič, J.: Semi-automatic Annotation Tool for Aspect-based Sentiment Analysis. FEI Technical University of Kosice, pp. 1–3 (2013)

    Google Scholar 

  8. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics 37(2), 267–307 (2011)

    Article  Google Scholar 

  9. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment Strength Detection in Short Informal Text. Journal of the American Society for Information Science and Technology 61(12), 2544–2558 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kristina Machova .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Machova, K., Marhefka, L. (2014). Opinion Classification in Conversational Content Using N-grams. In: Badica, A., Trawinski, B., Nguyen, N. (eds) Recent Developments in Computational Collective Intelligence. Studies in Computational Intelligence, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-01787-7_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-01787-7_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01786-0

  • Online ISBN: 978-3-319-01787-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics