基于多依赖图和知识融合的方面级情感分析模型
Aspect-based sentiment analysis model based on multi-dependency graph and knowledge fusion
何勇禧 1韩虎 1孔博1
作者信息
- 1. 兰州交通大学 电子与信息工程学院,甘肃 兰州 730070
- 折叠
摘要
方面级情感分析存在以下问题:句法依赖解析方式单一,语法信息的提取和利用不完善;外部知识库使用有限,无法提供足以判断情感的背景知识与信息;引入的知识过多,导致结论出现偏差.为此提出新的方面级情感分析模型,使用 2 种不同的句法解析方式对句子构建 2 种句法依赖图.依据外部情感知识构建情感关系图,引入概念知识图谱增强句子中的方面词本体,构建与经过概念知识图谱增强的句子对应的可见矩阵.使用双通道图卷积神经网络处理依赖图、情感关系图与可视矩阵,融合依赖图与情感关系图,对特定方面的特征表示进行语义、句法双交互.实验结果表明,所提模型在多个数据集上的准确率和宏F1 值均显著优于主流模型.
Abstract
The problems existing in aspect-based sentiment analysis include:a singular approach to syntactic dependency parsing,incomplete extraction and utilization of grammatical information;limited use of external knowledge bases,which failed to provide sufficient background knowledge and information for judging sentiment;and an excess of introduced knowledge,leading to biased conclusions.A new aspect-based sentiment analysis model was proposed,and two different syntactic parsing methods were utilized to construct two types of syntactic dependency graphs for sentences.Emotional dependency graphs were built based on external emotional knowledge,incorporating conceptual knowledge graphs to enhance aspect terms in sentences,constructing visible matrices corresponding to the sentences enhanced through conceptual knowledge graphs.A dual-channel graph convolutional neural network was employed to process the dependency graphs,the emotional dependency graphs and the visible matrices,integrating the dependency graphs with the emotional dependency graphs to perform semantic and syntactic dual interactions on specific aspect feature representations.Experimental results showed that the proposed model significantly outperformed the mainstream models in terms of accuracy and macro F1 score on multiple datasets.
关键词
方面级情感分析/多依赖图/知识图谱/图卷积网络/情感知识/概念知识Key words
aspect-based sentiment analysis/multi-dependency graph/knowledge graph/graph convolution network/affective knowledge/conceptual knowledge引用本文复制引用
出版年
2024
NETL
NSTL
万方数据