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.

[2021-11-05] Printed Article /doc/2021.04.0150/v2