SOUKHRIA: Towards an Irony Detection System for Arabic in Social Media | Zendy

Jihen Karoui | Zendy; Farah Banamara Zitoune | Zendy; Véronique Moriceau | Zendy

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SOUKHRIA: Towards an Irony Detection System for Arabic in Social Media

Author(s) -

Jihen Karoui,

Farah Banamara Zitoune,

Véronique Moriceau

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.10.105

Subject(s) - arabic , irony , classifier (uml) , computer science , artificial intelligence , natural language processing , binary classification , social media , linguistics , support vector machine , world wide web , philosophy

International audienceThis 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%

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