An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework
This addresses the challenge of automated sentiment analysis for sarcastic expressions in user-generated content, but it is incremental as it combines existing methods.
The paper tackles sarcasm detection in social media texts by proposing a hybrid model combining CNN, GRU, LSTM, and Multi-Head Attention, achieving accuracies of 81.20% on Headlines and 79.72% on Riloff datasets.
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.