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The role of predicting sarcasm in the text is known as automatic sarcasmdetection. Given the prevalence and challenges of sarcasm in sentiment-bearingtext, this is a critical phase in most sentiment analysis tasks. With theincreasing popularity and usage of different social media platforms among usersaround the world, people are using sarcasm more and more in their day-to-dayconversations, social media posts and tweets, and it is considered as a way forpeople to express their sentiment about some certain topics or issues. As aresult of the increasing popularity, researchers started to focus theirresearch endeavors on detecting sarcasm from a text in different languagesespecially the English language. However, the task of sarcasm detection is achallenging task due to the nature of sarcastic texts; which can be relativeand significantly differs from one person to another depending on the topic,region, the user's mentality and other factors. In addition to thesechallenges, sarcasm detection in the Arabic language has its own challenges dueto the complexity of the Arabic language, such as being morphologically rich,with many dialects that significantly vary between each other, while also beinglowly resourced. In recent years, only few research attempts started tacklingthe task of sarcasm detection in Arabic, including creating and collectingcorpora, organizing workshops and establishing baseline models. This paperintends to create a new humanly annotated Arabic corpus for sarcasm detectioncollected from tweets, and implementing a new approach for sarcasm detectionand quantification in Arabic tweets. The annotation technique followed in thispaper is unique in sarcasm detection and the proposed approach tackles theproblem as a regression problem instead of classification; i.e., the modelattempts to predict the level of sarcasm instead of binary classification.

sarcasm detection and quantification in arabic tweets