Computer Science ›› 2023, Vol. 50 ›› Issue (5): 230-237.doi: 10.11896/jsjkx.220300008

• Artificial Intelligence • Previous Articles     Next Articles

Aspect-based Sentiment Analysis Based on Dual-channel Graph Convolutional Network with Sentiment Knowledge

YANG Ying1, ZHANG Fan1,2, LI Tianrui1,2,3   

  1. 1 School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China
    2 Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province,Chengdu 611756,China
    3 National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Chengdu 611756,China
  • Received:2022-03-01 Revised:2022-09-01 Online:2023-05-15 Published:2023-05-06
  • About author:YANG Ying,born in 1997,postgra-duate,is a member of China Computer Federation.Her main research interests include sentiment analysis and natural language processing.
    LI Tianrui,born in 1969,Ph.D,professor,Ph.D supervisor,is a distinguished member of China Computer Federation.His main research interests include big data intelligence,rough sets,granular computing.
  • Supported by:
    National Natural Science Foundation of China(62176221).

Abstract: Aspect-based sentiment analysis is a fine-grained sentiment analysis task whose goal is to classify the sentiment polarity of the given aspect terms in a sentence.Most of the current sentiment classification models build a graph neural network on the dependency syntax tree,and learn the information between the aspect terms and the context from the dependency syntax tree,and lack the mining of sentiment knowledge in the sentence.To solve this problem,this paper proposes a sentiment classification model based on dual-channel graph convolutional network with sentiment knowledge.The model consists of a sentiment-enhanced dependency graph convolutional network(SDGCN) and an attention graph convolutional network(AGCN),which learn the syntactic dependencies and semantic relations of aspect terms and context words,respectively.Specifically,SDGCN incorporates sentiment knowledge from SenticNet on syntactic dependencies to enhance sentence dependencies,so that the model considers the syntactic relationship between context and aspects,together with the sentiment information between opinion words in the context and aspect terms.The attention mechanism is used by AGCN to learn the semantic relevance between aspect terms and the context in the sentence.Finally,the two graph convolution networks learn their own information interactively for sentiment classification.Experimental results show that the proposed model performs well on multiple public datasets,and ablation experiments verify the effectiveness of each module.

Key words: Aspect-based sentiment analysis, Sentiment knowledge, Dependencies, Graph convolutional networks, Attention mecha-nism

CLC Number: 

  • TP391
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