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International Conference on Big Data Innovations and Applications

Innovate-Data 2019: Big Data Innovations and Applications pp 107–117Cite as

Satire Detection in Turkish News Articles: A Machine Learning Approach

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1054))

Abstract

With the advances in information and communication technologies, an immense amount of information has been shared on social media and microblogging platforms. Much of the online content contains elements of figurative language, such as, irony, sarcasm and satire. The automatic identification of figurative language can be viewed as a challenging task in natural language processing, where linguistic entities, such as, metaphor, analogy, ambiguity, irony, sarcasm, satire, and so on, have been utilized to express more complex meanings. The predictive performance of sentiment classification schemes may degrade if figurative language within the text has not been properly addressed. Satirical text is a way of figurative communication, where ideas/opinions regarding a people, event or issue is expressed in a humorous way to criticize that entity. Satirical news can be deceptive and harmful. In this paper, we present a machine learning based approach to satire detection in Turkish news articles. In the presented scheme, we utilized three kinds of features to model lexical information, namely, unigrams, bigrams and tri-grams. In addition, term-frequency, term-presence and TF-IDF based schemes have been taken into consideration. In the classification phase, Naïve Bayes, support vector machines, logistic regression and C4.5 algorithms have been examined.

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Author information

Authors and Affiliations

  1. Faculty of Technology, Department of Software Engineering, Manisa Celal Bayar University, 45400, Manisa, Turkey

    Mansur Alp Toçoğlu

  2. Faculty of Engineering and Architecture, Department of Computer Engineering, İzmir Katip Çelebi University, 35620, İzmir, Turkey

    Aytuğ Onan

Authors
  1. Mansur Alp Toçoğlu
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  2. Aytuğ Onan
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Corresponding author

Correspondence to Aytuğ Onan .

Editor information

Editors and Affiliations

  1. School of ECM, Oxford Brookes University, Oxford, UK

    Muhammad Younas

  2. Department of Informatics, University of Bradford , Bradford, UK

    Irfan Awan

  3. Universite Paris Descartes, Paris, France

    Salima Benbernou

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Toçoğlu, M.A., Onan, A. (2019). Satire Detection in Turkish News Articles: A Machine Learning Approach. In: Younas, M., Awan, I., Benbernou, S. (eds) Big Data Innovations and Applications. Innovate-Data 2019. Communications in Computer and Information Science, vol 1054. Springer, Cham. https://doi.org/10.1007/978-3-030-27355-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-27355-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27354-5

  • Online ISBN: 978-3-030-27355-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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