Sarcasm Detection in Arabic Tweets: A comparison Between deep learning and Pre trained Transformers-based Models

R. Bouguesri, Khadidja Habelhames, H. Aliane, A. A. Aliane
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Abstract

Sarcasm is one of the main challenges of sentiment analysis systems. This paper mainly focuses on the recognition of Arabic sarcasm on Twitter. Recognizing sarcasm in tweets is essential for understanding users' opinions on various topics and events. There are only a few attempts regarding saracsm detection in Arabic due to the challenges and complexity of the Arabic language. We propose in this paper a comparison between traditional neural network-based models and pre-trained transformers. The experimental results show that transformers are a promising approach for the task of Arabic sarcasm detection.
阿拉伯语推文中的讽刺检测:深度学习和基于预训练的transformer模型的比较
讽刺是情感分析系统面临的主要挑战之一。本文主要研究Twitter上阿拉伯语讽刺语的识别问题。识别推文中的讽刺对于理解用户对各种话题和事件的看法至关重要。由于阿拉伯语的挑战和复杂性,只有少数尝试在阿拉伯语中检测讽刺语。本文提出了传统的基于神经网络的模型与预训练变压器的比较。实验结果表明,变压器是一种很有前途的阿拉伯语讽刺检测方法。
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