Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 2
411-416

Aspect-based sentiment analysis based on attention network

Shen Bina,b
Fang Yiquana,b
Cai Yuana
Cheng Huab
Zhong Yeb
a. Informatization Office, b. School of Information Science & Engineering, East China University of Science & Technology, Shanghai 200237, China
DOI: 10.19734/j.issn.1001-3695.2021.07.0297

Abstract

Traditional aspect-based sentiment analysis methods lack the research on the interaction between aspect object and its context, which leads to the low accuracy of sentiment analysis. In order to effectively extract richer text features, this paper proposed an aspect-based sentiment analysis model(IIMAN) to improve the effect of sentiment polarity judgment, which used multi-head attention mechanism to learn the interaction between aspect object and its context. Firstly, the model used the BERT pre-training model to complete the word vectorization of the input sentences. Then, it used the intra multi-head attention network and the inter multi-head attention network in the attention network to learn the interaction between aspect object and its context, as well as the internal interactions within the context. Finally, it realized the sentiment polarity classification through the pointwise convolution transfer layer, the aspect object attention layer and the output softmax layer. Through the experiments on three public aspect-level sentiment analysis datasets: Twitter, laptop and restaurant, the results show that, compared with other baseline models, IIMAN model achieves better effect in accuracy and F1 value, improves the results of the sentiment polarity classification.

Foundation Support

赛尔网络下一代互联网技术创新资助项目(NGII20170520)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.07.0297
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 2
Section: Algorithm Research & Explore
Pages: 411-416
Serial Number: 1001-3695(2022)02-015-0411-06

Publish History

[2021-10-18] Accepted Paper
[2022-02-05] Printed Article

Cite This Article

沈斌, 房一泉, 蔡源, 等. 基于注意力网络的属性级别情感分析 [J]. 计算机应用研究, 2022, 39 (2): 411-416. (Shen Bin, Fang Yiquan, Cai Yuan, et al. Aspect-based sentiment analysis based on attention network [J]. Application Research of Computers, 2022, 39 (2): 411-416. )

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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