In view of most current studies on text sentiment classification focus on the deep learning model to obtain the sentimental characteristics of English text. Chinese text sentiment analysis is rarely involved, and only the context information of the statement is considered, but the syntax information of the statement is rarely considered. In this paper, a novel sentiment classification model is proposed (Dependency Tree Graph Convolutional Network, DTGCN) combined Chinese syntactically dependent tree with graph convolution. Firstly, the Bi-GRU (Bi-directional Gated Recurrent Unit) model is used to learn the contextual feature representation of a given text. Secondly, the syntax-dependent tree structure of a given text is constructed, then obtain its adjacency matrix according to the syntax-dependent tree, with the initial features extracted from the bidirectional gate control network, input into the graph convolutional neural network (GCN) to extract the sentimental features of the text; the obtained sentimental characteristics are then input into the classifier SoftMax for text sentimental polarity classification. Finally, the data set is compared with the mainstream neural network model. The experimental results show that the accuracy of the proposed DTGCN model proposed on the data set is 90.51% and the recall rate is 90.34%. Compared with the benchmark models (LSTM, CNN, TextCNN and Bi-GRU), the proposed DTGCN model shows a 4.45% advantage in accuracy. It shows that the proposed DTGCN model can effectively use the grammatical information of Chinese text to mine the hidden relationship in statements, it can improve the accuracy of Chinese text sentiment classification. In addition, the proposed DTGCN model not only improves the performance of sentiment classification in the essay, it also provides a new research method for social network public opinion identification.

鉴于目前对文本情感分类的研究大多集中在深度学习模型以获取英文文本的情感特征。很少涉及中文文本情感分析，只考虑语句的上下文信息，很少考虑语句的语法信息。本文提出了一种新的情感分类模型（Dependency Tree Graph Convolutional Network，DTGCN），将中文句法依赖树与图卷积相结合。首先，使用 Bi-GRU（双向门控循环单元）模型来学习给定文本的上下文特征表示。其次，构造给定文本的句法依赖树结构，然后根据句法依赖树得到其邻接矩阵，将双向门控网络提取的初始特征输入到图卷积神经网络（GCN）中，提取文本的情感特征；然后将获得的情感特征输入到分类器SoftMax中进行文本情感极性分类。最后将数据集与主流的神经网络模型进行对比。实验结果表明，提出的DTGCN模型在数据集上的准确率为90.51%，召回率为90.34%。与基准模型（LSTM、CNN、TextCNN 和 Bi-GRU）相比，所提出的 DTGCN 模型在准确率上显示出 4.45% 的优势。表明所提出的 DTGCN 模型可以有效地利用中文文本的语法信息来挖掘语句中的隐藏关系，它可以提高中文文本情感分类的准确性。此外，所提出的DTGCN模型不仅提高了论文中情感分类的性能，还为社交网络舆情识别提供了一种新的研究方法。