GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification

doi:10.1016/j.eswa.2021.115712

	GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification
	Zhu, Xiaofei 1; Zhu, Ling 1; Guo, Jiafeng 2; Liang, Shangsong 3; Dietze, Stefan 4,5
	2021-12-30
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	186 页码:11
摘要	Aspect-based sentiment classification, which aims at identifying the sentiment polarity of a sentence towards the specified aspect, has become a crucial task for sentiment analysis. Existing methods have proposed effective models and achieved satisfactory results, but they mainly focus on exploiting local structure information of a given sentence, such as locality, sequentiality or syntactical dependency constraints within the sentence. Recently, some research works, which utilizes global dependency information, has attracted increasing interest and significantly boosts the performance of text classification. In this paper, we simultaneously introduce both global structure information and local structure information into the task of aspect-based sentiment classification, and propose a novel aspect-based sentiment classification approach, i.e., Global and Local Dependency Guided Graph Convolutional Networks (GL-GCN). In particular, we exploit the syntactic dependency structure as well as sentence sequential information (e.g., the output of BiLSTM) to mine the local structure information of a sentence. On the other hand, we construct a word-document graph using the entire corpus to reveal the global dependency information between words. In addition, an attention mechanism is leveraged to effectively fuse both global and local dependency structure signals. Extensive experiments are conducted on five benchmark datasets in terms of both Accuracy and F1-Score, and the results illustrate that our proposed framework outperforms state-of-the-art methods for aspect-based sentiment classification. The model is implemented using PyTorch and is trained on GPU GeForce GTX 2080 Ti.
关键词	Graph convolutional networks Aspect-based sentiment classification Attention mechanism Sentiment analysis
DOI	10.1016/j.eswa.2021.115712
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61722211] ; Federal Ministry of Education and Research, Germany[01LE1806A] ; Beijing Academy of Artificial Intelligence, China[BAAI2019ZD0306] ; Technology Innovation and Application Development of Chongqing, China[cstc2020jscx-dxwtBX 0014]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000704349700009
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：48[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16972
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhu, Xiaofei
作者单位	1.Chongqing Univ Technol, Coll Comp Sci & Engn, Chongqing 400054, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Peoples R China 4.Leibniz Inst Social Sci, Knowledge Technol Social Sci, D-50667 Cologne, Germany 5.Heinrich Heine Univ Dusseldorf, Inst Comp Sci, D-40225 Dusseldorf, Germany
推荐引用方式 GB/T 7714	Zhu, Xiaofei,Zhu, Ling,Guo, Jiafeng,et al. GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification[J]. EXPERT SYSTEMS WITH APPLICATIONS,2021,186:11.
APA	Zhu, Xiaofei,Zhu, Ling,Guo, Jiafeng,Liang, Shangsong,&Dietze, Stefan.(2021).GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification.EXPERT SYSTEMS WITH APPLICATIONS,186,11.
MLA	Zhu, Xiaofei,et al."GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification".EXPERT SYSTEMS WITH APPLICATIONS 186(2021):11.