A Comprehensive Survey on Arabic Sarcasm Detection: Approaches, Challenges and Future Trends

Author:

Rahma Alaa1ORCID,Azab Shahira Shaaban1ORCID,Mohammed Ammar1ORCID

Affiliation:

1. Department of Computer Science, Faculty of Graduate Studies for Statistical Research (FGSSR), Cairo University, Giza, Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Reference79 articles.

1. Multi-task learning using a combination of contextualised and static word embeddings for Arabic sarcasm detection and sentiment analysis;alharbi;Proc 6th Arabic Natural Lang Process Workshop,2021

2. ELECTRA: Pre-training text encoders as discriminators rather than generators;clark;arXiv 2003 10555,2020

3. AraBERT and Farasa segmentation based approach for sarcasm and sentiment detection in Arabic tweets;wadhawan;arXiv 2103 01679,2021

4. iCompass at shared task on sarcasm and sentiment detection in Arabic;naski;Proc 6th Arabic Natural Lang Process Workshop,2021

5. BENHA@IDAT: Improving irony detection in Arabic tweets using ensemble approach;nayel;Proc FIRE,2019

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