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Twitter Flu Trend: A Hybrid Deep Neural Network for Tweet Analysis

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Artificial Intelligence XXXIX (SGAI-AI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13652))

Abstract

Popular social networks such as Twitter have been proposed as a data source for public health monitoring because they have the potential to show infection disease surveillance like Influenza-Like Illnesses (ILI). However, shortness, data sparsity, informality, incorrect sentence structure, and the humorous are some challenges for tweet analysis and classification. In order to overcome these challenges and implement an accurate flu surveillance system, we propose a hybrid 1d-CNN-BiLSTM framework for semantic enrichment and tweet classification. Different embedding algorithms are compared for producing semantic representations of tweets to assist unrelated tweet filtering in the classification stage. We find that fine-tuning pre-trained Word2Vec enhances the model capability for representing the meaning of flu-related tweets than other embedding models. Our approach has been evaluated on a flu tweet dataset and compared with several baselines for tweet processing and classification. Experimental results show that: (1) the proposed hybrid deep neural networks can improve tweet classification due to considering their semantic information;(2) the proposed flu surveillance system achieves a state-of-the-art correlation coefficient with ILI rate published by CDC (https://www.cdc.gov/).

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References

  1. Allen, C., Tsou, M.H., Aslam, A., Nagel, A., Gawron, J.M.: Applying gis and machine learning methods to Twitter data for multiscale surveillance of influenza. PLoS ONE 11(7), e0157734 (2016)

    Article  Google Scholar 

  2. Aramaki, E., Maskawa, S., Morita, M.: Twitter catches the flu: detecting influenza epidemics using twitter. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1568–1576 (2011)

    Google Scholar 

  3. Broniatowski, D.A., Paul, M.J., Dredze, M.: National and local influenza surveillance through twitter: an analysis of the 2012–2013 influenza epidemic. PLoS ONE 8(12), e83672 (2013)

    Article  Google Scholar 

  4. Chen, T., Xu, R., He, Y., Wang, X.: Improving sentiment analysis via sentence type classification using bilstm-crf and cnn. Expert Syst. Appl. 72, 221–230 (2017)

    Article  Google Scholar 

  5. Doan, S., Ohno-Machado, L., Collier, N.: Enhancing twitter data analysis with simple semantic filtering: example in tracking influenza-like illnesses. In: 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology, pp. 62–71. IEEE (2012)

    Google Scholar 

  6. Dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 69–78 (2014)

    Google Scholar 

  7. Edouard, A., Cabrio, E., Tonelli, S., Le Thanh, N.: Semantic linking for event-based classification of tweets. Int. J. Comput. Linguist. Appl., 12 (2017)

    Google Scholar 

  8. Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012–1014 (2009)

    Article  Google Scholar 

  9. Goldberg, Y.: Neural network methods for natural language processing. Synthesis Lectures Hum. Lang. Technol. 10(1), 1–309 (2017)

    Article  Google Scholar 

  10. Jain, V.K., Kumar, S.: An effective approach to track levels of influenza-a (h1n1) pandemic in India using twitter. Procedia Comput. Sci. 70, 801–807 (2015)

    Article  Google Scholar 

  11. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)

    Google Scholar 

  12. Lamb, A., Paul, M., Dredze, M.: Separating fact from fear: tracking flu infections on twitter. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 789–795 (2013)

    Google Scholar 

  13. Li, H., Ji, H., Zhao, L.: Social event extraction: task, challenges and techniques. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 526–532 (2015)

    Google Scholar 

  14. Li, Y., Yang, T.: Word embedding for understanding natural language: a survey. In: Srinivasan, S. (ed.) Guide to Big Data Applications. SBD, vol. 26, pp. 83–104. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-53817-4_4

    Chapter  Google Scholar 

  15. Manaswi, N.K., Manaswi, N.K., John, S.: Deep learning with applications using python. Springer (2018)

    Google Scholar 

  16. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  17. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013)

    Google Scholar 

  18. Mikolov, T., Yih, W.t., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751 (2013)

    Google Scholar 

  19. Nowak, J., Taspinar, A., Scherer, R.: LSTM recurrent neural networks for short text and sentiment classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10246, pp. 553–562. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59060-8_50

    Chapter  Google Scholar 

  20. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  21. Ristoski, P., Paulheim, H.: Semantic web in data mining and knowledge discovery: a comprehensive survey. J. Web Semant. 36, 1–22 (2016)

    Article  Google Scholar 

  22. Saif, H., He, Y., Alani, H.: Semantic sentiment analysis of Twitter. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 508–524. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35176-1_32

    Chapter  Google Scholar 

  23. Schulz, A., Ristoski, P., Paulheim, H.: I see a car crash: real-time detection of small scale incidents in microblogs. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 22–33. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41242-4_3

    Chapter  Google Scholar 

  24. Varga, A., Basave, A.E.C., Rowe, M., Ciravegna, F., He, Y.: Linked knowledge sources for topic classification of microposts: a semantic graph-based approach. J. Web Semant. 26, 36–57 (2014)

    Article  Google Scholar 

  25. Velardi, P., Stilo, G., Tozzi, A.E., Gesualdo, F.: Twitter mining for fine-grained syndromic surveillance. Artif. Intell. Med. 61(3), 153–163 (2014)

    Article  Google Scholar 

  26. Wang, J., Yu, L.C., Lai, K.R., Zhang, X.: Dimensional sentiment analysis using a regional cnn-lstm model. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (volume 2: Short papers), pp. 225–230 (2016)

    Google Scholar 

  27. Wang, P., Xu, B., Xu, J., Tian, G., Liu, C.L., Hao, H.: Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification. Neurocomputing 174, 806–814 (2016)

    Article  Google Scholar 

  28. Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018)

    Google Scholar 

  29. Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: Lstm cells and network architectures. Neural Comput. 31(7), 1235–1270 (2019)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK

    Mahsa Abazari Kia

  2. University of Isfahan, Isfahan, Iran

    Fatemeh Ebrahimi Khaksefidi

Authors
  1. Mahsa Abazari Kia
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  2. Fatemeh Ebrahimi Khaksefidi
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Correspondence to Mahsa Abazari Kia .

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Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. DFKI: German Research Center for Artificial Intelligence, Oldenburg, Germany

    Frederic Stahl

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Abazari Kia, M., Ebrahimi Khaksefidi, F. (2022). Twitter Flu Trend: A Hybrid Deep Neural Network for Tweet Analysis. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XXXIX. SGAI-AI 2022. Lecture Notes in Computer Science(), vol 13652. Springer, Cham. https://doi.org/10.1007/978-3-031-21441-7_3

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  • DOI: https://doi.org/10.1007/978-3-031-21441-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21440-0

  • Online ISBN: 978-3-031-21441-7

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