融合多种模型的讽刺情感识别新方法，提升社交媒体文本分析准确率。
An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework
- 结合CNN、GRU、LSTM与多头注意力，捕捉局部特征与上下文依赖。
- 在Headlines数据集上达81.20%准确率和80.77%F1值，优于传统模型。
- 适合从事社交文本情感分析的研究者与应用开发者参考。
互联网与社交媒体的普及给自动情感分析带来挑战，尤其是用户生成内容中讽刺表达的识别。讽刺通过表面积极或夸张的语言传达负面情绪，增加了自然语言处理任务的难度。为此，我们提出一种创新的讽刺检测模型，整合卷积神经网络（CNN）、门控循环单元（GRU）、长短期记忆（LSTM）与多头注意力机制。CNN捕捉局部n-gram特征，GRU与LSTM建模序列依赖与上下文信息，多头注意力增强对输入关键部分的关注，提升可解释性。在Headlines与Riloff两个讽刺检测数据集上的实验表明，该模型在Headlines上取得81.20%准确率与80.77% F1值，在Riloff上达79.72%准确率与61.39% F1值，显著优于传统模型，验证了混合方法在社交媒体文本讽刺识别中的有效性。
原文摘要 · Abstract (English)
The pervasive use of the Internet and social media introduces significant challenges to automated sentiment analysis, particularly for sarcastic expressions in user-generated content. Sarcasm conveys negative emotions through ostensibly positive or exaggerated language, complicating its detection within natural language processing tasks. To address this, we propose an innovative sarcasm detection model integrating Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Multi-Head Attention mechanisms. The CNN component captures local n-gram features, while GRU and LSTM layers model sequential dependencies and contextual information. Multi-Head Attention enhances the model's focus on relevant parts of the input, improving interpretability. Experiments on two sarcasm detection datasets, Headlines and Riloff, demonstrate that the model achieves an accuracy of 81.20% and an F1 score of 80.77% on Headlines, and an accuracy of 79.72% with an F1 score of 61.39% on Riloff, outperforming traditional models. These results validate the effectiveness of our hybrid approach for sarcasm detection in social media texts.
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