Emotion classification of Chinese text using improved NEZHA model

Hao Yang; Lei Kuang; Chengjing Liang; Xuyi Lin

doi:10.1117/12.3051335

18 November 2024 Emotion classification of Chinese text using improved NEZHA model

Hao Yang, Lei Kuang, Chengjing Liang, Xuyi Lin

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134031U (2024) https://doi.org/10.1117/12.3051335
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

In this study, we enhance emotion classification for Chinese text by modifying the NEZHA model and evaluating various architectures. We developed and evaluated six distinct classifiers. Using the SMP2020-EWECT and ChnSentiCorp datasets, we assessed the models based on accuracy, F1 score, and loss. The DeepFeatureFusionClassifier and DeepFeatureRegClassifier emerged as the most effective models, with the DeepFeatureFusionClassifier achieving the highest performance on the ChnSentiCorp dataset and the DeepFeatureRegClassifier excelling on the SMP2020- EWECT dataset. The study highlights the effectiveness of advanced model architectures and multi-fold cross-validation on enhancing emotion classification accuracy and robustness.

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

Citation Download Citation

Hao Yang, Lei Kuang, Chengjing Liang, and Xuyi Lin "Emotion classification of Chinese text using improved NEZHA model", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134031U (18 November 2024); https://doi.org/10.1117/12.3051335

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