Sarcasm Detection Using Deep Convolutional Neural Networks: A Modular Deep Learning Framework
This addresses the problem of misinterpreted communication in text for applications such as chatbots and social media analysis, but it is incremental as it builds on existing methods.
The paper tackles sarcasm detection in text by proposing a modular deep learning framework that integrates DCNNs and BERT, showing feasibility for applications like chatbots and social media analysis.
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.