An Approach to Subjectivity Detection on Twitter Using the Structured Information

  • Juan SixtoEmail author
  • Aitor Almeida
  • Diego López-de-Ipiña
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9875)


In this paper, we propose an approach to the subjectivity detection on Twitter micro texts that explores the uses of the structured information of the social network framework. The sentiment analysis on Twitter has been usually performed through the automatic processing of the texts. However, the established limit of 140 characters and the particular characteristics of the texts reduce drastically the accuracy of Natural Language Processing (NLP) techniques. Under these circumstances, it becomes necessary to study new data sources that allow us to extract new useful knowledge to represent and classify the texts. The structured information, also called meta-information or meta-data, provide us with alternative features of the texts that can improve the classification tasks. In this study we have analysed the use of features extracted from the structured information in the subjectivity detection task, as a first step of the polarity detection task, and their integration with classical features.


Twitter Text categorization Data mining for social networks Subjectivity detection Social networks 


  1. 1.
    Alonso, M.A., Vilares, D.: A review on political analysis and social media. Procesamiento del Lenguaje Nat. 56, 13–24 (2016)Google Scholar
  2. 2.
    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, pp. 36–44 (2010)Google Scholar
  3. 3.
    Belkaroui, R., Faiz, R.: Towards events tweet contextualization using social influence model and users conversations. In: Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics, p. 3. ACM (2015)Google Scholar
  4. 4.
    Bermingham, A., Smeaton, A.F.: On using Twitter to monitor political sentiment and predict election results (2011)Google Scholar
  5. 5.
    Cotelo, J.M., Cruz, F., Ortega, F.J., Troyano, J.A.: Explorando Twitter mediante la integracin de informacin estructurada y no estructurada. Procesamiento del Lenguaje Nat. 55, 75–82 (2015)Google Scholar
  6. 6.
    Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using Twitter hashtags and smileys. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters (2010)Google Scholar
  7. 7.
    De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: ICWSM, p. 2 (2013)Google Scholar
  8. 8.
    Esparza, S.G., OMahony, M.P., Smyth, B.: Mining the real-time web: a novel approach to product recommendation. Knowl. Based Syst. 29, 3–11 (2012)CrossRefGoogle Scholar
  9. 9.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Han, B., Cook, P., Baldwin, T.: unimelb: Spanish text normalisation. In: Tweet-Norm@ SEPLN, pp. 32–36 (2013)Google Scholar
  11. 11.
    Harris, Z.S.: Distributional structure. Word 10(2–3), 146–162 (1954)CrossRefGoogle Scholar
  12. 12.
    Jeni, L.A., Cohn, J.F., De La Torre, F.: Facing imbalanced data-recommendations for the use of performance metrics. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), pp. 245–251. IEEE (2013)Google Scholar
  13. 13.
    Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent Twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 151–160 (2011)Google Scholar
  14. 14.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)CrossRefGoogle Scholar
  15. 15.
    Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C.C., Zhai, C.X. (eds.) Mining Text Data, pp. 415–463. Springer, New York (2012)CrossRefGoogle Scholar
  16. 16.
    Martínez-Cámara, E., Martín-Valdivia, M.T., Ureña-López, L.A., Montejo-Ráez, A.R.: Sentiment analysis in Twitter. Nat. Lang. Eng. 20(01), 1–28 (2014)CrossRefGoogle Scholar
  17. 17.
    Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)CrossRefGoogle Scholar
  18. 18.
    Mejova, Y., Srinivasan, P., Boynton, B.: GOP primary season on Twitter: popular political sentiment in social media. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. ACM (2013)Google Scholar
  19. 19.
    Mislove, A., Lehmann, S., Ahn, Y.Y., Onnela, J.P., Rosenquist, J.N.: Understanding the demographics of Twitter users. ICWSM 11, 5 (2011)Google Scholar
  20. 20.
    Monti, C., Rozza, A., Zapella, G., Zignani, M., Arvidsson, A., Colleoni, E.: Modelling political disaffection from Twitter data. In: Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM 2013) (2013)Google Scholar
  21. 21.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  22. 22.
    Pennacchiotti, M., Popescu, A.M.: A machine learning approach to Twitter user classification. ICWSM 11(1), 281–288 (2011)Google Scholar
  23. 23.
    Porta, J., Sancho, J.L.: Word normalization in Twitter using finite-state transducers. In: Tweet-Norm@ SEPLN, vol. 1086, pp. 49–53 (2013)Google Scholar
  24. 24.
    Smith, C.: DMR Twitter Statistic Report. Last modified 26 Feb 2016. Accessed 28 Mar 2016
  25. 25.
    Ting, K.M., Witten, I.H.: Issues in stacked generalization. J. Artif. Intell. Res. (JAIR) 10, 271–289 (1999)zbMATHGoogle Scholar
  26. 26.
    Villena-Román, J., García-Morera, J., García-Cumbreras, M.A., Martínez-Cámara, E., Martín-Valdivia, M.T., Ureã-López, L.A.: Overview of TASS 2015. In: Proceedings of TASS 2015: Workshop on Sentiment Analysis at SEPLN, vol. 1397. (2015)Google Scholar
  27. 27.
    Volkova, S., Wilson, T., Yarowsky, D.: Exploring demographic language variations to improve multilingual sentiment analysis in social media. In: EMNLP, pp. 1815–1827 (2013)Google Scholar
  28. 28.
    Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Juan Sixto
    • 1
    Email author
  • Aitor Almeida
    • 1
  • Diego López-de-Ipiña
    • 1
  1. 1.DeustoTech-Deusto Institute of TechnologyUniversidad de DeustoBilbaoSpain

Personalised recommendations