山西大学学报（自然科学版）2025，Vol.48Issue(4)：666-676,11.DOI:10.13451/j.sxu.ns.2025021
融合VAD知识的情感分布增强细粒度情绪识别方法
VAD Emotion Distribution Augmented Fine-grained Emotion Recognition
摘要
Abstract
Fine-grained emotion recognition models,which employ dozens of emotion categories to model human emotions,are ca-pable of capturing subtle emotional expressions more accurately than traditional models.However,existing emotion prediction mod-els have not fully considered the complex correlations that exist among the numerous fine-grained emotions.To address this issue,this paper proposes an VAD(Valence-Arousal-Dominance)Emotion Distribution Augmented Fine-grained Emotion Recognition(EDAER).EDAER models the emotional correlations in the VAD space using emotion distributions and combines textual semantic information with psychological priors for fine-grained emotion recognition.The EDAER model consists of three modules:a seman-tic information module,an emotion distribution information module,and a fusion prediction module.The semantic information mod-ule extracts textual semantic features using a pre-trained BERT(Bidirectional Encoder Representation from Transformers)model;the emotion distribution information module generates emotion distributions for emotion words based on VAD distance metrics to measure the similarity between emotions;and the fusion prediction module integrates textual semantic features and emotion distribu-tion information through an attention mechanism to predict emotions.Experimental results on the GoEmotions dataset demonstrate that the macro-average F1 score of the EDAER model reaches 51.75％,outperforming both the KEA(Knowledge-Embedded Atten-tion)model,which uses emotion lexicons as external knowledge,and the HGCN-EC(Hierarchy Graph Convolution Networks based Emotion Recognition)model,which utilizes hierarchical emotion relationships as external knowledge.Notably,for three emotion categories with fewer samples,EDAER significantly outperforms other models in terms of F1 score.These results validate that mod-eling emotional correlations in the VAD space through emotion distributions can effectively capture knowledge related to rare emo-tions,thus improving the model's ability to recognize fine-grained emotions.关键词
VAD情绪空间/外部知识/情绪分类/GoEmotionsKey words
VAD emotion space/external knowledge/emotion classification/GoEmotions分类
信息技术与安全科学引用本文复制引用
李春阳,万中英,曾雪强,左家莉,王明文..融合VAD知识的情感分布增强细粒度情绪识别方法[J].山西大学学报（自然科学版）,2025,48(4):666-676,11.基金项目
国家自然科学基金(62266021) （62266021）
江西省教育厅科学技术研究(GJJ2200330) （GJJ2200330）
