Sentiment classification model based on BERT and dependency syntax
In recent years,aspect-level sentiment classification methods based on deep learning models have become main-stream,especially graph neural network models based on syntactic structures,which have attracted extensive attention from re-searchers.However,most existing models do not fully utilize syntactic trees and cannot accurately understand the semantics of the text.To address these issues,a sentiment classification model based on BERT and dependency syntax is proposed.Experimental re-sults show that compared to traditional machine learning methods and ordinary deep learning methods,the proposed model achieves significant improvements in accuracy,recall,and F1-score metrics.
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