Advertisement

User Mood Tracking for Opinion Analysis on Twitter

  • Giuseppe CastellucciEmail author
  • Danilo Croce
  • Diego De Cao
  • Roberto Basili
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10037)

Abstract

The huge variability of trends, community interests and jargon is a crucial challenge for the application of language technologies to Social Media analysis. Models, such as grammars and lexicons, are exposed to rapid obsolescence, due to the speed at which topics as well as slogans change during time. In Sentiment Analysis, several works dynamically acquire the so-called opinionated lexicons. These are dictionaries where information regarding subjectivity aspects of individual words are described. This paper proposes an architecture for dynamic sentiment analysis over Twitter, combining structured learning and lexicon acquisition. Evidence about the beneficial effects of a dynamic architecture is reported through large scale tests over Twitter streams in Italian.

Keywords

Social media analytics Sentiment analysis Opinion mining Polarity lexicons 

References

  1. 1.
    Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of EMNLP, pp. 1631–1642. ACL (2013)Google Scholar
  2. 2.
    Poria, S., Cambria, E., Gelbukh, A., Bisio, F., Hussain, A.: Sentiment data flow analysis by means of dynamic linguistic patterns. IEEE CIM 10(4), 26–36 (2015)Google Scholar
  3. 3.
    Zhu, L., Galstyan, A., Cheng, J., Lerman, K.: Tripartite graph clustering for dynamic sentiment analysis on social media. In: Proceedings of the 2014 ICMD, pp. 1531–1542. ACM (2014)Google Scholar
  4. 4.
    Esuli, A., Sebastiani, F.: SentiWordNet: a publicly available lexical resource for opinion mining. In: Proceedings of 5th LREC, pp. 417–422 (2006)Google Scholar
  5. 5.
    Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the EACL, pp. 675–682. ACL (2009)Google Scholar
  6. 6.
    Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. JAIR 50, 723–762 (2014)zbMATHGoogle Scholar
  7. 7.
    Castellucci, G., Croce, D., Basili, R.: Acquiring a large scale polarity lexicon through unsupervised distributional methods. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds.) NLDB 2015. LNCS, vol. 9103, pp. 73–86. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-19581-0_6 CrossRefGoogle Scholar
  8. 8.
    Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL (2007)Google Scholar
  9. 9.
    Nguyen, L.T., Wu, P., Chan, W., Peng, W., Zhang, Y.: Predicting collective sentiment dynamics from time-series social media. In: Proceedings of the 1st Workshop on Issues of Sentiment Discovery and Opinion Mining, pp. 6:1–6:8. ACM (2012)Google Scholar
  10. 10.
    Zhang, Y., Mao, W., Zeng, D., Zhao, N., Bao, X.: Exploring opinion dynamics in security-related microblog data. In: JISIC, pp. 284–287 (2014)Google Scholar
  11. 11.
    Liu, S., Cheng, X., Li, F., Li, F.: TASC: topic-adaptive sentiment classification on dynamic tweets. IEEE Trans. KDE 27(6), 1696–1709 (2015)Google Scholar
  12. 12.
    Landauer, T., Dumais, S.: A solution to Plato’s problem: the latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychol. Rev. 104, 211–240 (1997)CrossRefGoogle Scholar
  13. 13.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)Google Scholar
  14. 14.
    Stone, P.J., Dunphy, D.C., Smith, M.S., Ogilvie, D.M.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge (1966)Google Scholar
  15. 15.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of EMNLP. ACL (2005)Google Scholar
  16. 16.
    Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: Proceedings of CAAGET Workshop (2010)Google Scholar
  17. 17.
    Mikolov, T., Yih, W.t., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of NAACL, pp. 746–751 (2013)Google Scholar
  18. 18.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Technical report, Stanford (2009)Google Scholar
  19. 19.
    Harris, Z.: Distributional structure. In: Katz, J.J., Fodor, J.A. (eds.) The Philosophy of Linguistics. Oxford University Press, Oxford (1964)Google Scholar
  20. 20.
    Baroni, M., Dinu, G., Kruszewski, G.: Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. In: Proceedings of ACL, pp. 238–247 (2014)Google Scholar
  21. 21.
    Sahlgren, M.: The word-space model. Ph.D. thesis, Stockholm University (2006)Google Scholar
  22. 22.
    Golub, G., Kahan, W.: Calculating the singular values and pseudo-inverse of a matrix. J. Soc. Ind. Appl. Math. Ser. B Numer. Anal. 2(2), 205–224 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)zbMATHGoogle Scholar
  24. 24.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, New York (2004)CrossRefzbMATHGoogle Scholar
  25. 25.
    Basile, V., Bolioli, A., Nissim, M., Patti, V., Rosso, P.: Overview of the Evalita 2014 sentiment polarity classification task. In: Proceedings of the 4th EVALITA (2014)Google Scholar
  26. 26.
    Basili, R., Zanzotto, F.M.: Parsing engineering and empirical robustness. Nat. Lang. Eng. 8(3), 97–120 (2002)Google Scholar
  27. 27.
    Filice, S., Castellucci, G., Croce, D., Basili, R.: KeLP: a kernel-based learning platform for natural language processing. In: Proceedings of ACL-IJCNLP, pp. 19–24 (2015)Google Scholar
  28. 28.
    Wang, Z., Vucetic, S.: Online passive-aggressive algorithms on a budget. JMLR 9, 908–915 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Giuseppe Castellucci
    • 1
    Email author
  • Danilo Croce
    • 2
  • Diego De Cao
    • 1
  • Roberto Basili
    • 2
  1. 1.Reveal s.r.l.RomeItaly
  2. 2.Department of Enterprise EngineeringUniversity of Roma Tor VergataRomeItaly

Personalised recommendations