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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 4
1049-1053

MASGC: simple graph convolutional emotion analysis model with specific masking

Jiang Yutong
Qian Xuezhong
Song Wei
School of Artificial Intelligence & Computer Science, Jiangnan University, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2021.09.0400

Abstract

At present, aspect-based sentiment analysis based on graph convolution neural network(GCN) integrates the grammatical structure of sentences. It can effectively solve the problem of inaccurate long-range lexical dependence. However, GCN has unnecessary complexity and redundant computing. In addition, it ignores the relative position relationship between attributes and context. This paper proposed a new model to solve these problems. Firstly, the model established a bidirectional GRU layer. Secondly, it used position-aware transformation to increase the importance of context words close to aspect words, and then reduced the complexity by removing nonlinearity and folding the weight matrix between successive layers. Thirdly, it fused the specific mask layer to realize the single-layer MASGC(masked simple graph convolutional networks) structure, and then generated a new attention mechanism based on retrieval context. Finally, it gave the classification results through the full connection layer. The model carries out a large number of experiments on five data sets. The experimental results show that it has higher accuracy and less training time.

Foundation Support

国家自然科学基金资助项目(62076110)
江苏省自然科学基金资助项目(BK20181341)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.09.0400
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 4
Section: Algorithm Research & Explore
Pages: 1049-1053
Serial Number: 1001-3695(2022)04-015-1049-05

Publish History

[2021-12-09] Accepted Paper
[2022-04-05] Printed Article

Cite This Article

姜宇桐, 钱雪忠, 宋威. MASGC：融合特定屏蔽机制的简单图卷积情感分析模型 [J]. 计算机应用研究, 2022, 39 (4): 1049-1053. (Jiang Yutong, Qian Xuezhong, Song Wei. MASGC: simple graph convolutional emotion analysis model with specific masking [J]. Application Research of Computers, 2022, 39 (4): 1049-1053. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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