Abstract
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.
Abstract (translated)
社会媒体中检测讽刺语的复杂性增加了语言多样性和讽刺表达的性质。现有模型有效解释阿拉伯语中的讽刺的能力存在显著的差距，这迫使需要更复杂和精确的检测方法。在本文中，我们研究了基本预处理组件对讽刺语音检测的影响。尽管表情符号在减轻现代通信中肢体语言和面部表情缺失效应方面起着关键作用，但它们对自动文本分析（特别是讽刺检测）的影响仍没有被深入研究。我们研究了表情符号从数据集中排除对阿拉伯语讽刺检测模型性能的影响。这项调查包括使用AraBERT预训练模型进行调整和增强，特别是通过排除表情符号，以提高讽刺检测能力。我们使用AraBERT预训练来优化指定模型，证明删除表情符号可以显著提高讽刺检测的准确性。这种方法使得对语言的解读更加精准，消除了非文本元素可能引起的混淆。评估的AraBERT模型通过移除表情符号，巧妙地处理了阿拉伯语讽刺的复杂性。本研究为阿拉伯自然语言处理设立了新的基准，并为社交媒体平台提供了宝贵的洞见。
URL
https://arxiv.org/abs/2405.02195
