Sentiment Spreading: An Epidemic Model for Lexicon-Based Sentiment Analysis on Twitter

  • Laura PollacciEmail author
  • Alina Sîrbu
  • Fosca Giannotti
  • Dino Pedreschi
  • Claudio Lucchese
  • Cristina Ioana Muntean
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10640)


While sentiment analysis has received significant attention in the last years, problems still exist when tools need to be applied to microblogging content. This because, typically, the text to be analysed consists of very short messages lacking in structure and semantic context. At the same time, the amount of text produced by online platforms is enormous. So, one needs simple, fast and effective methods in order to be able to efficiently study sentiment in these data. Lexicon-based methods, which use a predefined dictionary of terms tagged with sentiment valences to evaluate sentiment in longer sentences, can be a valid approach. Here we present a method based on epidemic spreading to automatically extend the dictionary used in lexicon-based sentiment analysis, starting from a reduced dictionary and large amounts of Twitter data. The resulting dictionary is shown to contain valences that correlate well with human-annotated sentiment, and to produce tweet sentiment classifications comparable to the original dictionary, with the advantage of being able to tag more tweets than the original. The method is easily extensible to various languages and applicable to large amounts of data.



This work has been funded by the European project SoBigData Research Infrastructure - Big Data and Social Mining Ecosystem under the INFRAIA-H2020 program (grant agreement 654024).


  1. 1.
    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, pp. 30–38. ACL (2011)Google Scholar
  2. 2.
    Bradley, M.M., Lang, P.J.: Affective norms for English words (ANEW): Instruction manual and affective ratings. Technical report. Citeseer (1999)Google Scholar
  3. 3.
    Castellano, C., Muñoz, M.A., Pastor-Satorras, R.: Nonlinear q-voter model. Phys. Rev. E 80(4), 041129 (2009)CrossRefGoogle Scholar
  4. 4.
    Ceron, A., Curini, L., Iacus, S.: Using social media to fore-cast electoral results: a review of state-of-the-art. Italian J. Appl. Stat. 25(3), 237–259 (2015)Google Scholar
  5. 5.
    Coletto, M., Esuli, A., Lucchese, C., Muntean, C.I., Nardini, F.M., Perego, R., Renso, C.: Perception of social phenomena through the multidimensional analysis of online social networks. Online Soc. Netw. Media 1, 14–32 (2017)CrossRefGoogle Scholar
  6. 6.
    Dodds, P.S., Danforth, C.M.: Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J. Happiness Stud. 11(4), 441–456 (2010)CrossRefGoogle Scholar
  7. 7.
    Esuli, A., Sebastiani, F.: SentiWordNet: a high-coverage lexical resource for opinion mining. Evaluation 1–26 (2007)Google Scholar
  8. 8.
    Gebremeskel, G.: Sentiment analysis of Twitter posts about news. Ph.D. thesis, Department of Computer Science and Artificial Intelligence, University of Malta (2011)Google Scholar
  9. 9.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report Stanford 1(12) (2009)Google Scholar
  10. 10.
    Guerini, M., Gatti, L., Turchi, M.: Sentiment analysis: how to derive prior polarities from SentiWordNet. arXiv preprint arXiv:1309.5843 (2013)
  11. 11.
    Hu, X., Tang, L., Tang, J., Liu, H.: Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 537–546. ACM (2013)Google Scholar
  12. 12.
    Khuc, V.N., Shivade, C., Ramnath, R., Ramanathan, J.: Towards building large-scale distributed systems for Twitter sentiment analysis. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 459–464. ACM (2012)Google Scholar
  13. 13.
    Mulcrone, K.: Detecting emotion in text. University of Minnesota-Morris CS Senior Seminar Paper (2012)Google Scholar
  14. 14.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends® Inf. Retrieval 2(1–2), 1–135 (2008)Google Scholar
  15. 15.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. ACL (2002)Google Scholar
  16. 16.
    Schmid, H.: Part-of-speech tagging with neural networks. In: Proceedings of the 15th Conference on Computational Linguistics, vol. 1, pp. 172–176. ACL (1994)Google Scholar
  17. 17.
    Sîrbu, A., Loreto, V., Servedio, V.D., Tria, F.: Opinion dynamics: models, extensions and external effects. In: Loreto, V., et al. (eds.) Participatory Sensing, Opinions and Collective Awareness. UCS, pp. 363–401. Springer, Heidelberg (2017).
  18. 18.
    Speriosu, M., Sudan, N., Upadhyay, S., Baldridge, J.: Twitter polarity classification with label propagation over lexical links and the follower graph. In: Proceedings of the First Workshop on Unsupervised Learning in NLP, pp. 53–63. ACL (2011)Google Scholar
  19. 19.
    Velikovich, L., Blair-Goldensohn, S., Hannan, K., McDonald, R.: The viability of web-derived polarity lexicons. In: HLT-NAACL, pp. 777–785 (2010)Google Scholar
  20. 20.
    Vertovec, S.: The emergence of super-diversity in Britain. Centre of Migration, Policy and Society, University of Oxford (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Laura Pollacci
    • 1
    Email author
  • Alina Sîrbu
    • 1
  • Fosca Giannotti
    • 2
  • Dino Pedreschi
    • 1
  • Claudio Lucchese
    • 2
  • Cristina Ioana Muntean
    • 2
  1. 1.Department of Computer ScienceUniversity of PisaPisaItaly
  2. 2.ISTI-CNRPisaItaly

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