  The role of predicting sarcasm in the text is known as automatic sarcasm
detection. Given the prevalence and challenges of sarcasm in sentiment-bearing
text, this is a critical phase in most sentiment analysis tasks. With the
increasing popularity and usage of different social media platforms among users
around the world, people are using sarcasm more and more in their day-to-day
conversations, social media posts and tweets, and it is considered as a way for
people to express their sentiment about some certain topics or issues. As a
result of the increasing popularity, researchers started to focus their
research endeavors on detecting sarcasm from a text in different languages
especially the English language. However, the task of sarcasm detection is a
challenging task due to the nature of sarcastic texts; which can be relative
and significantly differs from one person to another depending on the topic,
region, the user's mentality and other factors. In addition to these
challenges, sarcasm detection in the Arabic language has its own challenges due
to the complexity of the Arabic language, such as being morphologically rich,
with many dialects that significantly vary between each other, while also being
lowly resourced. In recent years, only few research attempts started tackling
the task of sarcasm detection in Arabic, including creating and collecting
corpora, organizing workshops and establishing baseline models. This paper
intends to create a new humanly annotated Arabic corpus for sarcasm detection
collected from tweets, and implementing a new approach for sarcasm detection
and quantification in Arabic tweets. The annotation technique followed in this
paper is unique in sarcasm detection and the proposed approach tackles the
problem as a regression problem instead of classification; i.e., the model
attempts to predict the level of sarcasm instead of binary classification.
