RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that mainly judges the polarity of a given aspect word in a review. Current methods mainly use graph networks to do aspect-level sentiment classification tasks, most of which use syntactic or semantic graphs, and utiliz...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/23/12108 |
| _version_ | 1797463626378903552 |
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| author | Huan Xu Shuxian Liu Wei Wang Le Deng |
| author_facet | Huan Xu Shuxian Liu Wei Wang Le Deng |
| author_sort | Huan Xu |
| collection | DOAJ |
| description | Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that mainly judges the polarity of a given aspect word in a review. Current methods mainly use graph networks to do aspect-level sentiment classification tasks, most of which use syntactic or semantic graphs, and utilize attention mechanisms to interact and correlate aspect terms and contexts to obtain more useful feature representations. However, these methods may ignore some insignificant syntactic structures and some implicit information in some sentences. The attention mechanism then easily loses the original information, which eventually leads to inaccurate sentiment analysis. In order to solve this problem, this paper proposes a model based on residual attention gating and three-channel graph convolutional network (RAG-TCGCN). Firstly, the model uses a three-channel network composed of syntactic information, semantic information, and public information to simultaneously optimize and fuse through the multi-head attention mechanism to solve the problem of sentences without significant syntactic structure and with implicit information. Through the residual attention gating mechanism the problem of loss of original information is solved. Experimental verification shows that the accuracy and F1 value of the model are improved on the three public datasets. |
| first_indexed | 2024-03-09T17:54:21Z |
| format | Article |
| id | doaj.art-90bc7dac31a0433083182458255409ca |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:54:21Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-90bc7dac31a0433083182458255409ca2023-11-24T10:31:06ZengMDPI AGApplied Sciences2076-34172022-11-0112231210810.3390/app122312108RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional NetworksHuan Xu0Shuxian Liu1Wei Wang2Le Deng3College of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaAspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that mainly judges the polarity of a given aspect word in a review. Current methods mainly use graph networks to do aspect-level sentiment classification tasks, most of which use syntactic or semantic graphs, and utilize attention mechanisms to interact and correlate aspect terms and contexts to obtain more useful feature representations. However, these methods may ignore some insignificant syntactic structures and some implicit information in some sentences. The attention mechanism then easily loses the original information, which eventually leads to inaccurate sentiment analysis. In order to solve this problem, this paper proposes a model based on residual attention gating and three-channel graph convolutional network (RAG-TCGCN). Firstly, the model uses a three-channel network composed of syntactic information, semantic information, and public information to simultaneously optimize and fuse through the multi-head attention mechanism to solve the problem of sentences without significant syntactic structure and with implicit information. Through the residual attention gating mechanism the problem of loss of original information is solved. Experimental verification shows that the accuracy and F1 value of the model are improved on the three public datasets.https://www.mdpi.com/2076-3417/12/23/12108residual attention gating mechanism (RAG)three-channel networkgraph convolution |
| spellingShingle | Huan Xu Shuxian Liu Wei Wang Le Deng RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks Applied Sciences residual attention gating mechanism (RAG) three-channel network graph convolution |
| title | RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks |
| title_full | RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks |
| title_fullStr | RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks |
| title_full_unstemmed | RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks |
| title_short | RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks |
| title_sort | rag tcgcn aspect sentiment analysis based on residual attention gating and three channel graph convolutional networks |
| topic | residual attention gating mechanism (RAG) three-channel network graph convolution |
| url | https://www.mdpi.com/2076-3417/12/23/12108 |
| work_keys_str_mv | AT huanxu ragtcgcnaspectsentimentanalysisbasedonresidualattentiongatingandthreechannelgraphconvolutionalnetworks AT shuxianliu ragtcgcnaspectsentimentanalysisbasedonresidualattentiongatingandthreechannelgraphconvolutionalnetworks AT weiwang ragtcgcnaspectsentimentanalysisbasedonresidualattentiongatingandthreechannelgraphconvolutionalnetworks AT ledeng ragtcgcnaspectsentimentanalysisbasedonresidualattentiongatingandthreechannelgraphconvolutionalnetworks |