Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6902086 | Procedia Computer Science | 2017 | 8 Pages |
Abstract
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%.
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Jihen Karoui, Farah Banamara Zitoune, VĂ©ronique Moriceau,