Multimodel fusion sentiment analysis based on adversarial training and contrastive learning

Xiangxiang Shi; Jianzhao Cao

doi:10.1117/12.3052489

19 November 2024 Multimodel fusion sentiment analysis based on adversarial training and contrastive learning

Xiangxiang Shi, Jianzhao Cao

Author Affiliations +

Proceedings Volume 13397, Fourth International Conference on Green Communication, Network, and Internet of Things (CNIoT 2024); 133970Y (2024) https://doi.org/10.1117/12.3052489
Event: 4th International Conference on Green Communication, Network, and Internet of Things (CNIoT 2024), 2024, Guiyang, China

Abstract

This study aims to address challenges in sentiment text classification, such as emotional shifts, contextual dual meanings, and the limitations of traditional models in robustness and feature extraction. We propose a neural network model that integrates Bert, BiLSTM, TextCNN, and Attention mechanisms, along with adversarial training and contrastive learning methods. Bert is used for word embedding, BiLSTM captures sentence dependencies, TextCNN extracts local features, and Attention mechanisms finely adjust semantic relationships. Fast Gradient Method (FGM) enhances robustness, and a Dual Contrastive Learning (DualCL) strategy improves feature representation. Experimental results show that the model significantly outperforms traditional methods and other deep learning models in accuracy and robustness.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiangxiang Shi and Jianzhao Cao "Multimodel fusion sentiment analysis based on adversarial training and contrastive learning", Proc. SPIE 13397, Fourth International Conference on Green Communication, Network, and Internet of Things (CNIoT 2024), 133970Y (19 November 2024); https://doi.org/10.1117/12.3052489

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