  The complex challenge of detecting sarcasm in Arabic speech on social media
is increased by the language diversity and the nature of sarcastic expressions.
There is a significant gap in the capability of existing models to effectively
interpret sarcasm in Arabic, which mandates the necessity for more
sophisticated and precise detection methods. In this paper, we investigate the
impact of a fundamental preprocessing component on sarcasm speech detection.
While emojis play a crucial role in mitigating the absence effect of body
language and facial expressions in modern communication, their impact on
automated text analysis, particularly in sarcasm detection, remains
underexplored. We investigate the impact of emoji exclusion from datasets on
the performance of sarcasm detection models in social media content for Arabic
as a vocabulary-super rich language. This investigation includes the adaptation
and enhancement of AraBERT pre-training models, specifically by excluding
emojis, to improve sarcasm detection capabilities. We use AraBERT pre-training
to refine the specified models, demonstrating that the removal of emojis can
significantly boost the accuracy of sarcasm detection. This approach
facilitates a more refined interpretation of language, eliminating the
potential confusion introduced by non-textual elements. The evaluated AraBERT
models, through the focused strategy of emoji removal, adeptly navigate the
complexities of Arabic sarcasm. This study establishes new benchmarks in Arabic
natural language processing and presents valuable insights for social media
platforms.
