An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework

TL;DR

This paper introduces a hybrid neural network model combining CNN, GRU, LSTM, and Multi-Head Attention for improved sarcasm detection in social media texts, demonstrating superior performance on benchmark datasets.

Contribution

The paper presents an innovative multi-component model that effectively captures local and sequential features for sarcasm recognition, utilizing the MindSpore framework for implementation.

Findings

Achieved 81.20% accuracy on Headlines dataset

Attained 80.77% F1 score on Headlines

Outperformed traditional sarcasm detection models

Abstract

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.