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Generally, the content of the utterance is the opposite of the context. Sentiment analysis tasks are hampered when a sarcastic tone is recognized in user-generated content. Thus, automatic sarcasm detection in textual data dramatically impacts the performance of sentiment analysis models. This study aims to explain the basic architecture of a sarcasm detection system and the most effective techniques for extracting sarcasm. Then, for the Arabic language, determining the gap and challenges.<\/jats:p>","DOI":"10.3233\/jifs-224514","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T12:10:59Z","timestamp":1690546259000},"page":"9483-9497","source":"Crossref","is-referenced-by-count":1,"title":["Automatic sarcasm detection in Arabic tweets: resources and approaches"],"prefix":"10.1177","volume":"45","author":[{"given":"Soukaina","family":"Mihi","sequence":"first","affiliation":[{"name":"Faculty of Sciences and Techniques, IR2M Laboratory, Hassan First University of Settat, Settat, Morocco"}]},{"given":"Brahim","family":"Ait Benali","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Techniques, IR2M Laboratory, Hassan First University of Settat, Settat, Morocco"}]},{"given":"Nabil","family":"Laachfoubi","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Techniques, IR2M Laboratory, Hassan First University of Settat, Settat, Morocco"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-224514_ref1","doi-asserted-by":"crossref","unstructured":"Shaalan K. , Siddiqui S. and Alkhatib M. , Chapter 3, Series on Language Processing, Pattern Recognition, and Intelligent Systems Computational Linguistics, Speech and Image Processing for Arabic Language, pp. 59\u201383 (2018). https:\/\/doi.org\/10.1142\/9789813229396_0003","DOI":"10.1142\/9789813229396_0003"},{"key":"10.3233\/JIFS-224514_ref2","unstructured":"Elkateb S. and Black W. , Arabic Word Net and the Challenges of Arabic, no. 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