Impact of Emojis Exclusion on the Performance of Arabic Sarcasm Detection Models

The complex challenge of detecting sarcasm in Arabic speech on social media is exacerbated by the language's diversity and the nature of sarcastic expressions. There is a significant gap in the capability of existing models to effectively interpret sarcasm in Arabic, necessitating more sophisticated and precise detection methods. In this paper, we investigate the impact of a fundamental preprocessing component on sarcasm detection. While emojis play a crucial role in mitigating the absence of body language and facial expressions in modern communication, their impact on automated text analysis, particularly in sarcasm detection, remains underexplored. We examine the effect of excluding emojis from datasets on the performance of sarcasm detection models in social media content for Arabic, a language with a super-rich vocabulary. This investigation includes the adaptation and enhancement of AraBERT pre-training models by specifically 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 emojis removal, adeptly navigate the complexities of Arabic sarcasm. This study establishes new benchmarks in Arabic natural language processing and offers valuable insights for social media platforms.