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Sarcasm Detection in Tunisian Social Media Comments: Case of COVID-19

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Foundations of Intelligent Systems (ISMIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13515))

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Abstract

The goal of this study is to learn more about how the public sees COVID-19 pandemic behaviors and to identify important themes of concern expressed by Tunisian social media users during the epidemic. Around 23K comments were collected, written in both Arabic and Latin characters in the Tunisian dialect. Native language experts manually tagged these comments for sarcasm identification (sarcastic and non-sarcastic). In addition to health, our dataset contains comments on entertainment, social, sports, religion, and politics, all of which are impacted by COVID-19. This research examines the sarcasm expressed in Tunisian social media comments regarding the novel COVID-19 from its appearance in the first half of 2020. We also provide benchmarking findings applying machine learning and deep learning algorithms for sarcasm detection. We obtained an accuracy of above 80%.

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Notes

  1. 1.

    https://www.who.int.

  2. 2.

    https://napoleoncat.com.

  3. 3.

    https://sentic.net.

  4. 4.

    https://exportcomments.com.

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Author information

Authors and Affiliations

  1. ANLP Research Group, MIRACL, University of Sfax, Sfax, Tunisia

    Asma Mekki, Inès Zribi, Mariem Ellouze & Lamia Hadrich Belguith

Authors
  1. Asma Mekki
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  2. Inès Zribi
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  3. Mariem Ellouze
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  4. Lamia Hadrich Belguith
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Corresponding author

Correspondence to Asma Mekki .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. Università della Calabria, Rende, Italy

    Sergio Flesca

  3. Università Federico II di Napoli, Naples, Italy

    Elio Masciari

  4. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

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Mekki, A., Zribi, I., Ellouze, M., Belguith, L.H. (2022). Sarcasm Detection in Tunisian Social Media Comments: Case of COVID-19. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_5

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  • DOI: https://doi.org/10.1007/978-3-031-16564-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16563-4

  • Online ISBN: 978-3-031-16564-1

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