1. Social network sentiment classification method combined Chinese text syntax with graph convolutional neural network
- Author
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Xiaoyang Liu, Ting Tang, and Nan Ding
- Subjects
Computer science ,Sentiment classification ,Word embedding ,02 engineering and technology ,Management Science and Operations Research ,computer.software_genre ,Convolutional neural network ,Text syntax ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,Adjacency matrix ,Graph convolutional neural network ,business.industry ,Deep learning ,Sentiment analysis ,020206 networking & telecommunications ,QA75.5-76.95 ,Computer Science Applications ,Tree (data structure) ,Tree structure ,Electronic computers. Computer science ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer ,Natural language processing ,Information Systems - Abstract
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.
- Published
- 2022