Aspect-based Sentiment Classification for Word Information Enhancement Based on Sentence Information
Aspect-based sentiment classification is a fine-grained sentiment classification task that aims to determine the senti-ment 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 neu-ral 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 senti-ment classification results.Experimental results on the SemEva12014 dataset and Twitter dataset demonstrate that the model im-proves classification accuracy,which verifies the effectiveness of our approach.
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