| SHAO Qing(邵清),ZHANG Wenshuang,WANG Shaojun.[J].高技术通讯(英文),2023,29(3):325~334 |
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| End-to-end aspect category sentiment analysis based on type graph convolutional networks |
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| DOI：10. 3772/ j. issn. 1006-6748. 2023. 03. 012 |
| 中文关键词: |
| 英文关键词: aspect-based sentiment analysis(ABSA), bidirectional encoder representation from transformers (BERT),type graph convolutional network(TGCN),aspect category and sentiment pair extraction |
| 基金项目: |
| Author Name | Affiliation | | SHAO Qing(邵清) | (School of Optoelectronic Information and Intelligent Engineering,University of Shanghai for Science and Technology,Shanghai 200093,P. R. China) | | ZHANG Wenshuang | | | WANG Shaojun | |
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| 中文摘要: |
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| 英文摘要: |
| For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction. This paper proposes an end-to-end aspect category sentiment analysis (ETESA) model based on type graph convolutional networks. The model uses the bidirectional encoder representation from transformers (BERT) pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy; when using graph convolutional network (GCN) for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation; by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation. Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model. |
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