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Autori principali: Zambre, Manas, Bobade, Sarika
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2510.10729
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author Zambre, Manas
Bobade, Sarika
author_facet Zambre, Manas
Bobade, Sarika
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2510_10729
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Sarcasm Detection Using Deep Convolutional Neural Networks: A Modular Deep Learning Framework
Zambre, Manas
Bobade, Sarika
Computation and Language
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.
title Sarcasm Detection Using Deep Convolutional Neural Networks: A Modular Deep Learning Framework
topic Computation and Language
url https://arxiv.org/abs/2510.10729