Sarcasm Detection Using Deep Convolutional Neural Networks: A Modular Deep Learning Framework

Manas Zambre; Sarika Bobade

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arXiv · 2510.10729

Sarcasm Detection Using Deep Convolutional Neural Networks: A Modular Deep Learning Framework

Manas Zambre·Sarika Bobade

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Abstract

Sarcasm is a nuanced and often misinterpreted form of communication, especially in text, where tone and body language are absent. This paper proposes a modular deep learning framework for sarcasm detection, leveraging Deep Convolutional Neural Networks (DCNNs) and contextual models such as BERT to analyze linguistic, emotional, and contextual cues. The system integrates sentiment analysis, contextual embeddings, linguistic feature extraction, and emotion detection through a multi-layer architecture. While the model is in the conceptual stage, it demonstrates feasibility for real-world applications such as chatbots and social media analysis.

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BibTeX RIS

Manas Zambre, Sarika Bobade. 2025-10-12. Sarcasm Detection Using Deep Convolutional Neural Networks: A Modular Deep Learning Framework. https://arxiv.org/abs/2510.10729

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