基于距离与图卷积网络的方面级情感分析

目前,基于卷积神经网络和循环神经网络的方面级情感分析研究工作较少同时考虑到句子的句法结构和词语的语法距离,且卷积神经网络和循环神经网络无法有效地处理图结构的数据.针对上述问题,提出了一种基于距离与图卷积网络的方面级情感分类模型.首先,为该模型设计了一个具有残差连接的双层双向长短期记忆网络,用于提取句子的上下文信息;然后,根据句法依赖树得到词语的语法距离权重,并根据词语之间的句法关系构建邻接矩阵;最后,采用图卷积网络结合句子的上下文信息、语法距离权重和邻接矩阵提取方面的情感特征.实验结果表明,模型是有效的且可获得更好的性能.

Aspect level sentiment analysis based on distance and graph convolution network

At present, there are few researches on aspect level sentiment analysis based on convolutional neural network and recurrent neural network, which take into account the syntactic structure of sentences and the syntax distance of words, and convolutional neural network and recurrent neural network can not effectively deal with the data of graph structure. To solve these problems, this paper proposed an aspect level sentiment classification model based on distance and graph convolution network. Firstly, this paper designed a two-layer bi-directional long short-term memory network with residual connection for the model to extract the context information of sentences. Then, the model obtained the weight of syntax distance of words and constructed the adjacency matrix from the syntactic dependency tree. Finally, the model used the graph convolution network to extract the sentiment features from context information, syntax distance weight and adjacency matrix. The experimental results show that the model is effective and can get better performance.