Skip to main content

Automatic Characteristics Extraction for Sentiment Analysis Tasks

  • Conference paper
  • First Online:
Computer Science – CACIC 2017 (CACIC 2017)

Abstract

The following article proposes the use of Open Information Extraction Methods (OIE), in particular ClausIE, to automatically obtain characteristics from movie reviews. Within automatic summary generation and sentiment analysis frameworks, this approach is compared with other two in which manual steps are used to obtain the characteristics of a service or product. The obtained result shows that ClausIE can be used for the extraction of characteristics in a semi-automatic way. It requires a minimum manual intervention that is explained in the results section.

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

Access this chapter

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 EPUB and 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

Institutional subscriptions

References

  1. Zhuang, L., Jing, F., Zhu, X.Y.: Movie review mining and summarization. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 43–50. ACM, November 2006

    Google Scholar 

  2. Del Corro, L., Gemulla, R.: ClausIE: sclause-based open information extraction. In Proceedings of the 22nd International Conference on World Wide Web, pp. 355–366. International World Wide Web Conferences Steering Committee, May 2013

    Google Scholar 

  3. García-Martínez, R., Britos, P.V.: Ingeniería de Sistemas Expertos. Nueva Librería, Buenos Aires (2004). ISBN 987-1104-15

    Google Scholar 

  4. Gómez, A., Juristo, N., Montes, C., Pazos, J.: Ingeniería del Conocimiento. Editorial Centro de Estudios Ramón Areces (1997). ISBN 84-8004-269-9

    Google Scholar 

  5. Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction for the web. In: IJCAI, vol. 7, pp. 2670–2676, January 2007

    Google Scholar 

  6. Rodríguez, J.M., Merlino, H., García-Martínez, R.: Revisión sistemática comparativa de evolución de métodos de extracción de conocimiento para la web. In: XXI Congreso Argentino de Ciencias de la Computación (CACIC 2015), Buenos Aires, Argentina (2015)

    Google Scholar 

  7. Rodríguez, J.M., Merlino, H.D., Pesado, P., García-Martínez, R.: Performance evaluation of knowledge extraction methods. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds.) IEA/AIE 2016. LNCS (LNAI), vol. 9799, pp. 16–22. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42007-3_2

    Google Scholar 

  8. Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1535–1545. Association for Computational Linguistics, July 2011

    Google Scholar 

  9. Schmitz, M., Bart, R., Soderland, S., Etzioni, O.: Open language learning for information extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 523–534. Association for Computational Linguistics, July 2012

    Google Scholar 

  10. Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G.A., Reynar, J.: Building a sentiment summarizer for local service reviews. In: WWW Workshop on NLP in the Information Explosion Era, vol. 14, pp. 339–348, April 2008

    Google Scholar 

  11. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics, July 2002

    Google Scholar 

  12. Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204, May 2010

    Google Scholar 

  13. Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., Rossiello, G.: SABRE: a sentiment aspect-based retrieval engine. In: Lai, C., Giuliani, A., Semeraro, G. (eds.) Information Filtering and Retrieval. SCI, vol. 668, pp. 63–78. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-46135-9_4

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Juan M. Rodríguez .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rodríguez, J.M., Merlino, H.D., García-Martínez, R. (2018). Automatic Characteristics Extraction for Sentiment Analysis Tasks. In: De Giusti, A. (eds) Computer Science – CACIC 2017. CACIC 2017. Communications in Computer and Information Science, vol 790. Springer, Cham. https://doi.org/10.1007/978-3-319-75214-3_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-75214-3_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75213-6

  • Online ISBN: 978-3-319-75214-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics