Tweet Classification Framework for Detecting Events Related to Health Problems

  • Marcin MajakEmail author
  • Andrzej Zolnierek
  • Katarzyna Wegrzyn
  • Lamine Bougueroua
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)


In this paper we present and validate the MC (Multiclassifier) system for Tweet classification related to flu and its symptoms. Proposed method consists of a preprocessing phase applying NLTK processor with converter from text corpora into feature space and as a last step ensemble of heterogenous classifiers fused at support level for Tweet classification. We have checked two methods for translating text into feature space. The first one uses standard Term Frequency times Inverse Document frequency, while the second one is enriched with hashtag analysis and word reduction after n-grams generation. Our preliminary results prove that Twitter can be an excellent platform for sensing real events. The most important task in proper event detection is a feature extraction technique taking into account not only text corpora, but also sentiment analysis and message intention.


Short message processing Tweet classification Event detection 



This work was supported by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE - European Research Centre of Network Intelligence for Innovation Enhancement ( This was also supported by the statutory funds of Department of Systems and Computer Networks, Wroclaw University of Technology. All computer experiments were carried out using computer equipment sponsored by ENGINE project.


  1. 1.
  2. 2.
    Aiello, L.M., Petkos, G., Martin, C., Corney, D., Papadopoulos, S., Skraba, R., Goker, A., Kompatsiaris, I., Jaimes, A.: Sensing trending topics in twitter. IEEE Trans. Multimedia 15(6), 1268–1282 (2013)CrossRefGoogle Scholar
  3. 3.
    Alpaydin, E.: Combined 5 \(\times \) 2 cv F test for comparing supervised classification learning algorithms. J. Neural Comput. 11, 1885–1892 (1999)CrossRefGoogle Scholar
  4. 4.
    Atefeh, F., Khreich, W.: A survey of techniques for event detection in twitter. Comput. Intell. 31(1), 132–164 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Batista, L.B., Ratte, S.: A multi-classifier system for sentiment analysis and opinion mining. In: Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), pp. 96–100. IEEE Computer Society, Washington, DC (2012)Google Scholar
  6. 6.
    Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media (2009)Google Scholar
  7. 7.
    Cavalin, P.R., Moyano, L.G., Miranda, P.P.: A multiple classifier system for classifying life events on social media. In: ICDM Workshops, pp. 1332–1335. IEEE Computer Society (2015)Google Scholar
  8. 8.
    Celikyilmaz, A., Hakkani-Tur, D., Feng, J.: Probabilistic model-based sentiment analysis of twitter messages, pp. 79–84. IEEE (2010)Google Scholar
  9. 9.
    Joachims, T.: Text categorization with suport vector machines: learning with many relevant features. In: Proceedings of the 10th European Conference on Machine Learning, ECML 1998, pp. 137–142. Springer, London (1998)Google Scholar
  10. 10.
    Kaleel, S.B., Abhari, A.: Cluster-discovery of twitter messages for event detection and trending. J. Comput. Sci. 6, 47–57 (2015)CrossRefGoogle Scholar
  11. 11.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: WWW 2010: Proceedings of the 19th International Conference on World Wide Web, NY, USA, pp. 591–600. ACM, New York (2010)Google Scholar
  12. 12.
    Lamb, A., Paul, M.J., Dredze, M.: Separating fact from fear: tracking flu infections on twitter. In: NAACL (2013)Google Scholar
  13. 13.
    Salathe, M., Khandelwal, S.: Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control. PLOS Comput. Biol. 7(10), 1–7 (10 2011)Google Scholar
  14. 14.
    Zubiaga, A., Spina, D.: Martínez, R., Fresno, V.: Real-time classification of twitter trends. J. Assoc. Inf. Sci. Technol. 66(3), 462–473 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Marcin Majak
    • 1
    Email author
  • Andrzej Zolnierek
    • 1
  • Katarzyna Wegrzyn
    • 2
  • Lamine Bougueroua
    • 2
  1. 1.Wrocław University of Science and TechnologyWrocławPoland
  2. 2.AllianSTIC, Efrei GroupVillejuifFrance

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