Analysis of the Structured Information for Subjectivity Detection in Twitter

  • Juan SixtoEmail author
  • Aitor Almeida
  • Diego López-de-Ipiña
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10840)


In this paper, we analyze the opportunities of the structured information of the social networks for the subjectivity detection on Twitter micro texts. 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 when compared with other domains. 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 paper we analyze the features of the structured information and their usefulness in the opinion mining sub-domain, specially in the subjectivity detection task. Also present a novel classification of these features according to their origin.


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



This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the project E-RMP (CSO2015-64495-R).


  1. 1.
    Alonso, M.A., Vilares, D.: A review on political analysis and social media. Procesamiento del Lenguaje Natural 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 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 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.
    Bosco, C., Patti, V., Bolioli, A.: Developing corpora for sentiment analysis: the case of irony and senti-TUT. IEEE Intell. Syst. 28(2), 55–63 (2013)CrossRefGoogle Scholar
  6. 6.
    Cerón-Guzmán, J.A.: JACERONG at TASS 2016: an ensemble classifier for sentiment analysis of Spanish tweets at global level. In: Proceedings of TASS 2016: Workshop on Sentiment Analysis at SEPLN co-located with 32nd SEPLN Conference (SEPLN 2016), pp. 35–39 (2016)Google Scholar
  7. 7.
    Cotelo, J.M., Cruz, F., Ortega, F.J., Troyano, J.A.: Explorando Twitter mediante la integración de información estructurada y no estructurada. Procesamiento del Lenguaje Natural 55, 75–82 (2015)Google Scholar
  8. 8.
    Cui, A., Zhang, M., Liu, Y., Ma, S.: Emotion tokens: bridging the gap among multilingual Twitter sentiment analysis. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds.) AIRS 2011. LNCS, vol. 7097, pp. 238–249. Springer, Heidelberg (2011). Scholar
  9. 9.
    Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: Proceedings of 23rd International Conference on Computational Linguistics: Posters (2010)Google Scholar
  10. 10.
    De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: ICWSM, p. 2 (2013)Google Scholar
  11. 11.
    Esparza, S.G., O’Mahony, M.P., Smyth, B.: Mining the real-time web: a novel approach to product recommendation. Knowl.-Based Syst. 29, 3–11 (2012)CrossRefGoogle Scholar
  12. 12.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001)Google Scholar
  13. 13.
    Giorgis, S., Rousas, A., Pavlopoulos, J., Malakasiotis, P., Androutsopoulos, I.: aueb.twitter.sentiment at SemEval-2016 task 4: a weighted ensemble of SVMs for Twitter sentiment analysis. In: Proceedings of SemEval, pp. 96–99 (2016)Google Scholar
  14. 14.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, vol. 1, no. 12 (2009)Google Scholar
  15. 15.
    Han, B., Cook, P., Baldwin, T.: Unimelb: Spanish text normalisation. In: Tweet-Norm@SEPLN, pp. 32–36 (2013)Google Scholar
  16. 16.
    Harris, Z.S.: Distributional structure. Word 10(2–3), 146–162 (1954)CrossRefGoogle Scholar
  17. 17.
    Hurtado, L.F., Pla, F., Buscaldi, D.: ELiRF-UPV en TASS 2015: Análisis de Sentimientos en Twitter. In: Proceedings of TASS 2015: Workshop on Sentiment Analysis at SEPLN Co-located with 31st SEPLN Conference (SEPLN 2015) (2015)Google Scholar
  18. 18.
    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
  19. 19.
    Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 151–160 (2011)Google Scholar
  20. 20.
    Joshi, M.V., Agarwal, M.C., Kumar, V.: Mining needle in a haystack: classifying rare classes via two-phase rule induction. ACM SIGMOD Rec. 30(2), 91–102 (2001)CrossRefGoogle Scholar
  21. 21.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)CrossRefGoogle Scholar
  22. 22.
    Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 415–463. Springer, Boston (2012). Scholar
  23. 23.
    Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(2), 539–550 (2009)CrossRefGoogle Scholar
  24. 24.
    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
  25. 25.
    Martínez-Cámara, E., Gutiérrez-Vázquez, Y., Fernández, J., Montejo-Ráez, A., Muñoz-Guillena, R.: Ensemble classifier for Twitter sentiment analysis (2015)Google Scholar
  26. 26.
    Martínez-Cámara, E., Martín-Valdivia, M.T., Ureña López, L.A., Mitkov, R.: Polarity classification for Spanish tweets using the COST corpus. J. Inf. Sci. 41(3), 263–272 (1015)CrossRefGoogle Scholar
  27. 27.
    Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)CrossRefGoogle Scholar
  28. 28.
    Mejova, Y., Srinivasan, P., Boynton, B.: GOP primary season on Twitter: popular political sentiment in social media. In: Proceedings of 6th ACM International Conference on Web Search and Data Mining. ACM (2013)Google Scholar
  29. 29.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  30. 30.
    Mislove, A., Lehmann, S., Ahn, Y.Y., Onnela, J.P., Rosenquist, J.N.: Understanding the demographics of Twitter users. In: ICWSM, vol. 11, no. 5 (2011)Google Scholar
  31. 31.
    Montejo-Ráez, A., Díaz-Galiano, M.C.: Participación de SINAI en TASS 2016. In: Proceedings of TASS 2016: Workshop on Sentiment Analysis at SEPLN (2016)Google Scholar
  32. 32.
    Monti, C., Rozza, A., Zapella, G., Zignani, M., Arvidsson, A., Colleoni, E.: Modelling political disaffection from Twitter data. In: Proceedings of 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM 2013) (2013)Google Scholar
  33. 33.
    Nabil, M., Atyia, A., Aly, M.: CUFE at SemEval-2016 task 4: a gated recurrent model for sentiment classification. In: Proceedings of 10th International Workshop on Semantic Evaluation (SemEval-2016) (2016)Google Scholar
  34. 34.
    Opitz, D., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169–198 (1999)zbMATHGoogle Scholar
  35. 35.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  36. 36.
    Park, S.: Sentiment classification using sociolinguistic clusters. In: Proceedings of TASS 2015: Workshop on Sentiment Analysis at SEPLN Co-located with 31st SEPLN Conference (SEPLN 2015), pp. 99–104 (2015)Google Scholar
  37. 37.
    Pennacchiotti, M., Popescu, A.M.: A machine learning approach to Twitter user classification. ICWSM 11(1), 281–288 (2011)Google Scholar
  38. 38.
    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
  39. 39.
    Schapire, R.E.: A brief introduction to boosting. IJCAI 99, 1401–1406 (1999)Google Scholar
  40. 40.
    Siordia, O.S., Guerrero, M.G., Avila, E.S.T., Jimenez, S.M., Moctezuma, D., García, E.A.V.: Sentiment analysis for Twitter: TASS 2015. In: Proceedings of TASS 2015: Workshop on Sentiment Analysis at SEPLN Co-located with 31st SEPLN Conference (SEPLN 2015) (2015)Google Scholar
  41. 41.
    Sixto, J., Almeida, A., López-de-Ipiña, D.: An approach to subjectivity detection on Twitter using the structured information. In: International Conference on Computational Collective Intelligence, Part 1, pp. 121–130 (2016)CrossRefGoogle Scholar
  42. 42.
    Sixto, J., Almeida, A., López-de-Ipiña, D.: Improving the sentiment analysis process of Spanish tweets with BM25. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds.) NLDB 2016. LNCS, vol. 9612, pp. 285–291. Springer, Cham (2016). Scholar
  43. 43.
    Smith, C.: DMR Twitter statistic report, Last Modified 26 Feb 2016. Accessed 28 Mar 2016
  44. 44.
    Ting, K.M., Witten, I.H.: Issues in stacked generalization. J. Artif. Intell. Res. (JAIR) 10, 271–289 (1999)zbMATHGoogle Scholar
  45. 45.
    Villena-Román, J., Lana-Serrano, S., Martínez-Cámara, E., González-Cristóbal, J.C.: TASS - workshop on sentiment analysis at SEPLN. Procesamiento del Lenguaje Natural 50, 37–44 (2013)Google Scholar
  46. 46.
    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
  47. 47.
    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
  48. 48.
    Weiss, G.M.: Mining with rarity: a unifying framework. ACM SIGKDD Explor. Newsl. 6(1), 7–19 (2004)CrossRefGoogle Scholar
  49. 49.
    Weiss, G.M., Provost, F.: Learning when training data are costly: the effect of class distribution on tree induction. J. Artif. Intell. Res. 19, 315–354 (2003)zbMATHGoogle Scholar
  50. 50.
    Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)CrossRefGoogle Scholar
  51. 51.
    Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Hoste, V.: SemEval-2016 task 5: aspect based sentiment analysis. In: ProWorkshop on Semantic Evaluation (SemEval-2016) (2016)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

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

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