Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Alpaydin, E.: Combined 5 \(\times \) 2 cv F test for comparing supervised classification learning algorithms. J. Neural Comput. 11, 1885–1892 (1999)
Atefeh, F., Khreich, W.: A survey of techniques for event detection in twitter. Comput. Intell. 31(1), 132–164 (2015)
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)
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media (2009)
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)
Celikyilmaz, A., Hakkani-Tur, D., Feng, J.: Probabilistic model-based sentiment analysis of twitter messages, pp. 79–84. IEEE (2010)
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)
Kaleel, S.B., Abhari, A.: Cluster-discovery of twitter messages for event detection and trending. J. Comput. Sci. 6, 47–57 (2015)
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)
Lamb, A., Paul, M.J., Dredze, M.: Separating fact from fear: tracking flu infections on twitter. In: NAACL (2013)
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)
Zubiaga, A., Spina, D.: Martínez, R., Fresno, V.: Real-time classification of twitter trends. J. Assoc. Inf. Sci. Technol. 66(3), 462–473 (2015)
Acknowledgments
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 (http://engine.pwr.wroc.pl/). 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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Majak, M., Zolnierek, A., Wegrzyn, K., Bougueroua, L. (2018). Tweet Classification Framework for Detecting Events Related to Health Problems. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-59162-9_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59161-2
Online ISBN: 978-3-319-59162-9
eBook Packages: EngineeringEngineering (R0)