用深度卷积网络检测文本讽刺，融合情感与上下文信息。
Sarcasm Detection Using Deep Convolutional Neural Networks: A Modular Deep Learning Framework
- 分模块设计，结合情感分析与上下文嵌入
- 通过多层架构整合语言与情绪特征
- 适合聊天机器人与社交媒体内容分析
讽刺是一种微妙且常被误解的沟通形式，尤其在缺乏语调和肢体语言的文本中。本文提出一种模块化深度学习框架，利用深度卷积神经网络（DCNNs）和上下文模型如BERT，分析语言、情感及上下文线索。系统通过多层结构集成情感分析、上下文嵌入、语言特征提取与情绪检测。尽管模型尚处于概念阶段，但已展现出在聊天机器人与社交媒体分析等实际应用中的可行性。
原文摘要 · Abstract (English)
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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