MGE-BERT:融合标签排序与情感图感知的多标签情感分类模型  

MGE-BERT:Multi-label Sentiment Classification Model Integrating Label Ranking and Sentiment Graph Perception

作　　者：关慧 韩志远 GUAN Hui;HAN Zhi-Yuan(College of Computer Science and Technology,Shenyang University of Chemical Technology,Shenyang 110142,China;Key Laboratory of Intelligent Technology for Chemical Process Industry in Liaoning Province,Shenyang 110142,China)

机构地区：[1]沈阳化工大学计算机科学与技术学院,沈阳110142 [2]辽宁省化工过程工业智能化技术重点实验室,沈阳110142

出　　处：《计算机系统应用》2025年第3期268-276,共9页Computer Systems & Applications

摘　　要：目前多标签文本分类研究已经整合了标签信息,但在情感分析领域,现有方法常忽略了基于情感本身强度和极性的标签相关性,这些相关性对于精确分类至关重要.为了解决这些问题,本文提出了多标签交互和情感图感知的MGE-BERT模型.该模型首先通过情感强度关联和情感层次关联对情感标签进行优先排序,然后将排序后的标签与文本数据结合,作为输入导入BERT模型.在此过程中,采用了句法分析技术和情感词典,通过独特的构图方法构建了复杂的依赖图和情感图.为了进一步增强标签信息与文本特征的深度融合,本文将BERT模型的输出作为图卷积网络(GCN)的输入,使其能够更精确地捕捉并传递节点间的上下文关系.实验结果表明,在SemEval2018Task-1C数据集和Go Emotions数据集上进行的实验中,本文提出的MGE-BERT模型相比于最先进的模型,Macro-F1得分分别提高了1.6%和2.0%.Currently,research on multi-label text classification integrates label information.However,in the field of sentiment analysis,existing methods often overlook the correlations of labels based on the intensity and polarity of emotions themselves,which are crucial for accurate classification.To address these issues,this study proposes the MGEBERT model which features multi-label interaction,graph enhancement,and emotion perception.The model first prioritizes sentiment label sorting through the correlations of sentiment intensity and hierarchy and then combines these sorted labels with text data as inputs into the BERT model.During this process,syntactic analysis techniques and sentiment lexicons are employed,and through a unique graph construction method,intricate dependency and emotion graphs are built.To further enhance the in-depth integration of label information and text features,the study uses BERT outputs as inputs to graph convolutional network(GCN),enabling it to capture and transmit contextual relationships between nodes more precisely.Experimental results demonstrate that the proposed MGE-BERT model outperforms stateof-the-art models,achieving improvements in Macro-F1 scores by 1.6%and 2.0%on the SemEval2018 Task-1C and GoEmotions datasets,respectively.

关 键 词：多标签情感分类 情感标签排序 情感图感知 情感词典 情感强度加权 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]