期刊
ARABIC COMPUTATIONAL LINGUISTICS (ACLING 2017)
卷 117, 期 -, 页码 161-168出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2017.10.105
关键词
Arabic tweets; opinion analysis; irony detection; supervised learning
资金
- French FUI (Fond d'Investissements d' Avenir de) SparkinData project
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%. (C) 2017 The Authors. Published by Elsevier B.V.
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