Social network sentiment classification method combined Chinese text syntax with graph convolutional neural network
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-03-01
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| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866521000268 |
| _version_ | 1819282866800951296 |
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| author | Xiaoyang Liu Ting Tang Nan Ding |
| author_facet | Xiaoyang Liu Ting Tang Nan Ding |
| author_sort | Xiaoyang Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-24T01:22:24Z |
| format | Article |
| id | doaj.art-0dfaac746a164f5eaa384582c235bf06 |
| institution | Directory Open Access Journal |
| issn | 1110-8665 |
| language | English |
| last_indexed | 2024-12-24T01:22:24Z |
| publishDate | 2022-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj.art-0dfaac746a164f5eaa384582c235bf062022-12-21T17:22:36ZengElsevierEgyptian Informatics Journal1110-86652022-03-01231112Social network sentiment classification method combined Chinese text syntax with graph convolutional neural networkXiaoyang Liu0Ting Tang1Nan Ding2Corresponding author.; School of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, ChinaSchool of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, ChinaSchool of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, ChinaIn 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.http://www.sciencedirect.com/science/article/pii/S1110866521000268Sentiment classificationGraph convolutional neural networkText syntaxWord embedding |
| spellingShingle | Xiaoyang Liu Ting Tang Nan Ding Social network sentiment classification method combined Chinese text syntax with graph convolutional neural network Egyptian Informatics Journal Sentiment classification Graph convolutional neural network Text syntax Word embedding |
| title | Social network sentiment classification method combined Chinese text syntax with graph convolutional neural network |
| title_full | Social network sentiment classification method combined Chinese text syntax with graph convolutional neural network |
| title_fullStr | Social network sentiment classification method combined Chinese text syntax with graph convolutional neural network |
| title_full_unstemmed | Social network sentiment classification method combined Chinese text syntax with graph convolutional neural network |
| title_short | Social network sentiment classification method combined Chinese text syntax with graph convolutional neural network |
| title_sort | social network sentiment classification method combined chinese text syntax with graph convolutional neural network |
| topic | Sentiment classification Graph convolutional neural network Text syntax Word embedding |
| url | http://www.sciencedirect.com/science/article/pii/S1110866521000268 |
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