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SOUKHRIA: Towards an irony detection system for arabic in social media

Karoui, Jihen and Benamara, Farah and Moriceau, Véronique SOUKHRIA: Towards an irony detection system for arabic in social media. (2017) Procedia Computer Science, 117. 161-168. ISSN 1877-0509

(Document in English)

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Official URL: https://doi.org/10.1016/j.procs.2017.10.105


This paper presents a supervised learning method for irony detection in Arabic tweets. A binary classifier uses four groups of features whose efficiency has been empirically proved in other languages such as French, English, Italian, Dutch and Japanese. Our first results are encouraging and show that state of the art features can be successfully applied to Arabic language with an accuracy of 72.76%.

Item Type:Article
Additional Information:Thanks to Elsevier editor. This papers appears in volume 117 of Procedia Computer Science ESSN: 1877-0509 The definitive version is available at: http://www.sciencedirect.com The original PDF of the article can be found at : https://www.sciencedirect.com/science/article/pii/S1877050917321622
Audience (journal):International peer-reviewed journal
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Université Paris-Saclay (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Université de Sfax (TUNISIA)
Other partners > Université Paris-Sud 11 (FRANCE)
Laboratory name:
Deposited On:12 May 2020 16:51

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