湖南大学学报（自然科学版）2024，Vol.51Issue(12)：165-175,11.DOI:10.16339/j.cnki.hdxbzkb.2024293
多特征加权图卷积网络的情感三元组抽取方法
Sentiment Triplet Extraction Method for Multi-feature Weighted Graph Convolutional Networks
摘要
Abstract
Aspect-based sentiment analysis(ABSA)aims to identify users'opinions expressed about specific text aspects using elements such as aspect words,opinion words,and sentiment polarity.However,the existing research mainly focuses on individual tasks,which neglects feature interactions between different parts and causes error propagation issues.A sentiment triplet extraction method based on a multi-feature weighted graph convolutional network is proposed to jointly model multiple subtasks.Then,a double affine attention module is employed to capture the relational probability distribution among word pairs.Additionally,prior information such as text semantics,syntax,and location is encoded into multi-feature vectors.Finally,graph convolution operations are utilized for achieving multi-feature fusion and realizing the joint extraction of aspect term-opinion term-sentiment polarity.Through the estimation test based on 2 benchmark datasets,the experimental results reveal that the sentiment triplet extraction method based on a multi-feature weighted graph convolutional network can effectively alleviate the error propagation issues in pipeline methods.Moreover,feature interaction among each factor of the triplet set is proposed,and it is proved that the model in the current work performs much better than the previous benchmark model at triplet extraction.关键词
情感分析/图神经网络/网格标记/双仿射注意力/联合抽取Key words
sentiment analysis/graph neural networks/grid tagging/biaffine attention/joint extraction分类
信息技术与安全科学引用本文复制引用
韩虎,徐学锋,赵启涛,范雅婷..多特征加权图卷积网络的情感三元组抽取方法[J].湖南大学学报（自然科学版）,2024,51(12):165-175,11.基金项目
国家自然科学基金资助项目(62166024),National Natural Science Foundation of China(62166024) （62166024）
