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        <title>Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic</title>
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    <abstract>Sarcasm detection is one of the top challenging tasks in text classification, particularly for informal Arabic with high syntactic and semantic ambiguity. We propose two systems that harness knowledge from multiple tasks to improve the performance of the classifier. This paper presents the systems used in our participation to the two sub-tasks of the Sixth Arabic Natural Language Processing Workshop (WANLP); Sarcasm Detection and Sentiment Analysis. Our methodology is driven by the hypothesis that tweets with negative sentiment and tweets with sarcasm content are more likely to have offensive content, thus, fine-tuning the classification model using large corpus of offensive language, supports the learning process of the model to effectively detect sentiment and sarcasm contents. Results demonstrate the effectiveness of our approach for sarcasm detection task over sentiment analysis task.</abstract>
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%0 Conference Proceedings
%T Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic
%A Husain, Fatemah
%A Uzuner, Ozlem
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Hajj, Hazem
%Y Magdy, Walid
%Y Zaghouani, Wajdi
%Y Bougares, Fethi
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Touileb, Samia
%S Proceedings of the Sixth Arabic Natural Language Processing Workshop
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv, Ukraine (Virtual)
%F husain-uzuner-2021-leveraging
%X Sarcasm detection is one of the top challenging tasks in text classification, particularly for informal Arabic with high syntactic and semantic ambiguity. We propose two systems that harness knowledge from multiple tasks to improve the performance of the classifier. This paper presents the systems used in our participation to the two sub-tasks of the Sixth Arabic Natural Language Processing Workshop (WANLP); Sarcasm Detection and Sentiment Analysis. Our methodology is driven by the hypothesis that tweets with negative sentiment and tweets with sarcasm content are more likely to have offensive content, thus, fine-tuning the classification model using large corpus of offensive language, supports the learning process of the model to effectively detect sentiment and sarcasm contents. Results demonstrate the effectiveness of our approach for sarcasm detection task over sentiment analysis task.
%U https://aclanthology.org/2021.wanlp-1.47
%P 364-369

Markdown (Informal)

[Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic](https://aclanthology.org/2021.wanlp-1.47) (Husain & Uzuner, WANLP 2021)

• Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic (Husain & Uzuner, WANLP 2021)

ACL

• Fatemah Husain and Ozlem Uzuner. 2021. Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 364–369, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.