DeepSarc: A Transformer‐Based Deep Learning Approach for Sarcasm Detection in Social Media Text

ABSTRACT Sarcasm detection is a critical and challenging task in sentiment analysis, particularly for low‐resource languages like Urdu, where limited annotated data, linguistic complexity, and subtle contextual cues hinder accurate classification. Traditional machine learning methods often fail to capture the nuanced and often contradictory nature of sarcastic expression. To address these challenges, this paper presents a comprehensive and computationally efficient framework for Urdu sarcasm detection. We first mitigate severe class imbalance through strategic down‐sampling and back‐translation‐based data augmentation. We then conduct extensive benchmarking of traditional deep learning architectures against fine‐tuned pre‐trained language models, including multilingual, monolingual, and Twitter‐specific variants. Building on these insights, we propose DeepSarc, a novel hybrid model that integrates the contextual embeddings from XLM‐T, the multi‐scale feature extraction capabilities of dilated convolutional neural networks (DCNNs), and the sequential dependency modeling of bidirectional long short‐term memory (BiLSTM) networks. While slightly more computationally intensive than simpler alternatives, DeepSarc achieves a state‐of‐the‐art F1‐score, significantly outperforming existing approaches. Our results establish a new benchmark for sarcasm detection in low‐resource languages and provide a scalable, high‐performance framework adaptable to diverse linguistic contexts.