SS-GCN:情感增强和句法增强的方面级情感分析模型

SS-GCN:Aspect-based Sentiment Analysis Model with Affective Enhancement and Syntactic Enhancement

方面级情感分析(Aspect-Based Sentiment Analysis,ABSA)作为知识图谱下游应用,属于细粒度情感分析任务,旨在理解人们对评价目标在方面层次的情感极性。近年来,相关研究已经取得显著进步,但现有方法侧重于利用句子内的顺序性或句法依赖约束,而没有充分利用上下文词与方面词之间的依赖类型。此外,现有的基于图卷积神经网络模型对节点特征保留的能力不足。针对该问题,首先,在句法依赖树的基础上,充分挖掘上下文词与方面词之间的依赖类型,将其融入依赖图的构建;其次,定义了一个“敏感关系集合”,利用它来构建辅助句以增强特定上下文词与方面词之间的关联性,同时结合情感知识网络SenticNet以增强句子的依赖图,进而改进图神经网络的构建;最后,引入上下文保留机制,来减小节点特征在多层图卷积神经网络中的信息损失。提出的SS-GCN模型将并行学习到的句法表示和上下文表示进行融合以完成情感增强和句法增强。在3个公开数据集上进行了广泛的实验,证明了SS-GCN的有效性。

Aspect-based sentiment analysis(ABSA),as a downstream application of knowledge graph,belongs to the fine-grained sentiment analysis task,which aims to understand the sentiment polarity of people on the evaluation target at the aspect level.Relevant research in recent years has made significant progress,but existing methods focus on exploiting sequentiality or syntactic dependency constraints within sentences,and do not fully exploit the type of dependencies between context words and aspect words.In addition,the existing graph-based convolutional neural network models have insufficient ability to retain node features.In response to this problem,firstly,based on the syntactic dependency tree,this paper fully excavates the dependency types between context words and aspect words,and integrates them into the construction of the dependency graph.Second,we define a“sensitive relation set”,which is used to construct auxiliary sentences to enhance the correlation between specific context words and aspect words,and at the same time,combined with the sentiment knowledge network SenticNet to enhance the sentence dependency graph,and then improve the construction of the graph neural network.Finally,a context retention mechanism is introduced to reduce the information loss of node features in the multilayer graph convolution neural network.The proposed SS-GCN model fuses the syntactic and contextual representations learned in parallel to accomplish sentiment enhancement and syntactic enhancement,and extensive experiments on three public datasets demonstrate the effectiveness of SS-GCN.