Abstract Hidden emotional characteristics of the statement in various aspects can be discovered by aspect-level sentiment classification. Based on the framework of aspect-specific graph convolutional network, an aspect-level sentiment classification model based on context-preserving capability is proposed. A context gating unit is introduced into the graph convolution layer to reintegrate the useful information in the output of the previous layer. A multi-grained attention computing module is added to the proposed model to describe the interrelation in emotional expression between aspect words and their context. Experimental results on five public datasets show the advantages of the proposed model in classification accuracy and macro-F1.
Corresponding Authors:
HE Li, Ph.D., professor. Her research interests include data mining and machine learning.
About author:: FANG Wanlin, master student. Her research interests include natural language processing and sentiment analysis. ZHANG Hongyan, master student. Her research interests include computer vision and image segmentation. FANG Wanlin, master student. Her research interests include natural language processing and sentiment analysis. ZHANG Hongyan, master student. Her research interests include computer vision and image segmentation.
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2021, Vol. 34 
