Emotion classification of Chinese text using improved NEZHA model
Hao Yang, Lei Kuang, Chengjing Liang, Xuyi Lin · 2024
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.