Skip to main content

Content and Style Features for Automatic Detection of Users’ Intentions in Tweets

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8864)

Abstract

The aim of this paper is to evaluate the use of content and style features in automatic classification of intentions of Tweets. For this we propose different style features and evaluate them using a machine learning approach. We found that although the style features by themselves are useful for the identification of the intentions of tweets, it is better to combine such features with the content ones. We present a set of experiments, where we achieved a 9.46 % of improvement on the overall performance of the classification with the combination of content and style features as compared with the content features.

Keywords

  • Short texts
  • Text classification
  • Twitter
  • Detection of intention

This work was done under partial support of the Mexican Government (CONACYT-134186, CONACYT grant #308719, SNI, COFAA-IPN, SIP-IPN 20144274) and FP7-PEOPLE-2010-IRSES: “Web Information Quality Evaluation Initiative (WIQ-EI)” European Commission project 269180.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-12027-0_10
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-12027-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Wickre, K.: Celebrating #Twitter7. Twitter Blog (2013), https://blog.twitter.com/2013/celebrating-twitter7

  2. Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: Understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis. ACM (2007)

    Google Scholar 

  3. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of Twitter data. In: Proceedings of the Workshop on Languages in Social Media. ACL, Stroudsburg (2011)

    Google Scholar 

  4. Pandey, V., Iyer, C.: Sentiment analysis of microblogs. Technical report (2009)

    Google Scholar 

  5. Jain, H., Mogadala, A., Varma, V.: Sielers : Feature analysis and polarity classification of expressions from Twitter and SMS data. In: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013). Second Joint Conference on Lexical and Computational Semantics (*SEM), vol. 2. ACL (2013)

    Google Scholar 

  6. Asur, S., Huberman, B.A.: Predicting the future with social media. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1. IEEE Computer Society (2010)

    Google Scholar 

  7. Diakopoulos, N.A., Shamma, D.A.: Characterizing debate performance via aggregated Twitter sentiment. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM (2010)

    Google Scholar 

  8. Alhadi, A.C., Staab, S., Gottron, T.: Exploring user purpose writing single tweets. In: WebSci 2011: Proceedings of the 3rd International Conference on Web Science. ACM (2011)

    Google Scholar 

  9. Website: Wikipedia, the free encyclopedia (2014), http://en.wikipedia.org/wiki/Intention

  10. Martis, M., Alfaro, R.: Clasificación automática de la intención del usuario en mensajes de Twitter. In: I Workshop en Procesamiento Automatizado de Textos y Corpora, Sausalito, Viña del (March 2012)

    Google Scholar 

  11. Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in Twitter to improve information filtering. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2010)

    Google Scholar 

  12. Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol. 60(3) (2009)

    Google Scholar 

  13. Rangel, F., Rosso, P.: Use of language and author profiling: Identification of gender and age. Natural Language Processing and Cognitive Science (2013)

    Google Scholar 

  14. Barbosa, L., Feng, J.: Robust sentiment detection on Twitter from biased and noisy data. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters. ACL, pp. 36–44 (2010)

    Google Scholar 

  15. Naaman, M., Boase, J., Lai, C.H.: Is it really about me?: Message content in social awareness streams. In: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work. ACM (2010)

    Google Scholar 

  16. Banerjee, N., Chakraborty, D., Joshi, A., Mittal, S., Rai, A., Ravindran, B.: Towards analyzing micro-blogs for detection and classification of real-time intentions. In: Breslin, J.G., Ellison, N.B., Shanahan, J.G., Tufekci, Z., (eds.) Proceedings of International AAAI Conference on Weblogs and Social Media. The AAAI Press (2012)

    Google Scholar 

  17. Zhao, Y., Zobel, J.: Effective and Scalable Authorship Attribution Using Function Words. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.-H. (eds.) AIRS 2005. LNCS, vol. 3689, pp. 174–189. Springer, Heidelberg (2005)

    Google Scholar 

  18. Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., Chanona-Hernández, L.: Syntactic n-grams as machine learning features for natural language processing. Expert Syst. Appl. 41(3) (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Helena Gómez-Adorno .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Gómez-Adorno, H., Pinto, D., Montes, M., Sidorov, G., Alfaro, R. (2014). Content and Style Features for Automatic Detection of Users’ Intentions in Tweets. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12027-0_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12026-3

  • Online ISBN: 978-3-319-12027-0

  • eBook Packages: Computer ScienceComputer Science (R0)