Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (2): 83-89.doi: 10.6040/j.issn.1672-3961.0.2020.246

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Aspect-level sentiment classification combined with syntactic dependency information

ZHANG Qinyang, LI Xu*, YAO Chunlong, LI Changwu   

  1. School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, Liaoning, China
  • Published:2021-04-16

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Abstract: Considering introducing syntactic dependency information into the original aspect terms, a new aspect term representation method was proposed. First Glove word vector was used to represent the words and dependency relationship between words, and the dependency adjacency matrix and the representation of dependency relationship matrix including syntactic dependency information was constructed. Then graph convolution neural network and multi-head attention mechanism were used to integrate syntactic dependency information into aspect terms, so that aspect terms were highly related to context structure. The models generalization ability were effectively improved by replacing the existing models with the improved aspect term expression. Through comparative experiments and analysis, effectiveness and generalization of the method were proved.

Key words: syntactically dependency information, aspect-level sentiment classification, Glove word vector, graph convolution, attention mechanism

CLC Number: 

  • TP183
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