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

Abstract

On social media platforms, it is essential to express one’s thoughts, opinions, and reviews. One of the most widely used linguistic forms to criticize or express a person’s ideas with ridicule is sarcasm, where the written text has both intended and unintended meanings. The sarcastic text frequently reverses the polarity of the sentiment. Therefore, detecting sarcasm in the text has a positive impact on the sentiment analysis task and ensures more accurate results. Although Arabic is one of the most frequently used languages for web content sharing, the sarcasm detection of Arabic content is restricted and yet still naive due to several challenges, including the morphological structure of the Arabic language, the variety of dialects, and the lack of adequate data sources. Despite that, researchers started investigating this area by introducing the first Arabic dataset and experiment for irony detection in 2017. Thus, our review focuses on studies published between 2017 and 2022 on Arabic sarcasm detection. We provide a thorough literature review of Artificial Intelligence (AI) techniques and benchmarks used for Arabic sarcasm detection. In addition, the challenges of Arabic sarcasm detection are investigated, along with future directions, focusing on the challenge of publicly available Arabic sarcasm datasets.

Keywords

• Artificial intelligence (AI)
• Arabic sarcasm detection
• deep learning (DL)
• machine learning (ML)
• natural language processing (NLP)
• sentiment analysis (SA)