Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6): 299-308.doi: 10.11896/jsjkx.230600059

• Artificial Intelligence • Previous Articles     Next Articles

Aspect-based Sentiment Classification for Word Information Enhancement Based on Sentence Information

LI Yilin1, SUN Chengsheng2, LUO Lin3, JU Shenggen1   

  1. 1 College of Computer Science,Sichuan University,Chengdu 610065,China
    2 China Electronic Technology Cyber Security Co.,Ltd,Chengdu 610041,China
    3 No.30 Research Institute of CETC,Chengdu 610041,China
  • Received:2023-06-07 Revised:2023-11-25 Online:2024-06-15 Published:2024-06-05
  • About author:LI Yilin,born in 1997,postgraduate,is a member of CCF(No.K9226G).His main research interests include natural language processing and sentiment analysis.
    JU Shenggen,born in 1970,Ph.D,professor,Ph.D supervisor,is a senior member of CCF(No.14364S).His main research interests include data mining,natural language processing and know-ledge graph.
  • Supported by:
    National Natural Science Foundation of China(62137001).

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Abstract: Aspect-based sentiment classification is a fine-grained sentiment classification task that aims to determine the sentiment polarity of specified aspect terms in a sentence.In recent years,syntactic knowledge has been widely applied in the field of aspect-based sentiment classification.Current mainstream models utilize syntactic dependency trees and graph convolutional neural networks to classify sentiment polarity.However,these models primarily focus on using aggregated aspect term information to determine sentiment polarity,and few studies focus on the impact of global sentence information on sentiment polarity.This leads to biased sentiment classification results.To address this issue,this paper proposes an aspect-based sentiment classification model that enhances aspect term information with sentence-level information.This model learns sentence representations through con-trastive learning,with the goal of minimizing the contrastive loss of sentence vectors to adjust the feature representation of word vectors.Finally,the model aggregates opinion word information using a graph convolutional neural network(GCN)to obtain sentiment classification results.Experimental results on the SemEval2014 dataset and Twitter dataset demonstrate that the model improves classification accuracy,which verifies the effectiveness of our approach.

Key words: Aspect-based sentiment classification, Sentence information, Word information, Contrastive learning, Graph convolutional network

CLC Number: 

  • TP391
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