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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 11
3274-3278,3321

Aspect level sentiment analysis based on distance and graph convolution network

Wu Haosheng1a
Miao Yuqing1a,1b,1c
Zhang Wanzhen2
Zhou Ming3
Wen Yimin1a
1. a. School of Computer Science & Information Security, b. Key Laboratory of Image & Graphics Intelligent Processing, Guilin University of Electronic Technology, c. Guangxi Key Laboratory of Cryptography & Information Security, Guilin Guangxi 541004, China
2. Practice Teaching Dept. , Guilin University of Aerospace Technology, Guilin Guangxi 541004, China
3. Guilin Hivision Technology Company, Guilin Guangxi 541004, China
DOI: 10.19734/j.issn.1001-3695.2021.04.0150

Abstract

At present, there are few researches on aspect level sentiment analysis based on convolutional neural network and recurrent neural network, which take into account the syntactic structure of sentences and the syntax distance of words, and convolutional neural network and recurrent neural network can not effectively deal with the data of graph structure. To solve these problems, this paper proposed an aspect level sentiment classification model based on distance and graph convolution network. Firstly, this paper designed a two-layer bi-directional long short-term memory network with residual connection for the model to extract the context information of sentences. Then, the model obtained the weight of syntax distance of words and constructed the adjacency matrix from the syntactic dependency tree. Finally, the model used the graph convolution network to extract the sentiment features from context information, syntax distance weight and adjacency matrix. The experimental results show that the model is effective and can get better performance.

Foundation Support

国家自然科学基金资助项目(61763007,61866007)
广西自然科学基金重点项目(2017GXNSFDA198028)
广西自然科学基金资助项目(2020GXNSFAA159094)
桂林市科学技术局重大项目(科技攻关20170301)
广西密码学与信息安全重点实验室项目(GCIS201816)
广西高校图像图形智能处理重点实验室研究项目(GIIP201706)
广西高校中青年教师科研基础能力提升项目(2021KY0799)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.04.0150
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 11
Section: Algorithm Research & Explore
Pages: 3274-3278,3321
Serial Number: 1001-3695(2021)11-012-3274-05

Publish History

[2021-11-05] Printed Article

Cite This Article

巫浩盛, 缪裕青, 张万桢, 等. 基于距离与图卷积网络的方面级情感分析 [J]. 计算机应用研究, 2021, 38 (11): 3274-3278,3321. (Wu Haosheng, Miao Yuqing, Zhang Wanzhen, et al. Aspect level sentiment analysis based on distance and graph convolution network [J]. Application Research of Computers, 2021, 38 (11): 3274-3278,3321. )

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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