Syntactic Knowledge Embedding Network for Aspect-Based Sentiment Classification

Abstract

An aspect-based sentiment classification task is a fine-grained sentiment analysis task, which is aimed at identifying the sentiment polarity of a given aspect in subjective sentences. In recent years, some researchers have applied pretrained BERT models to this task. However, existing research only uses the BERT output layer and ignores the syntactic features in the middle layers, leading to deviations in the prediction results. In order to solve above problems, we propose a new model BERT-SFE. Firstly, we explicitly utilize the middle layers of BERT to capture the underlying syntactic features. Secondly, we construct a syntactic feature extraction unit based on Star-Transformer, which uses an auxiliary vector and the star network structure to capture both local and global syntactic information in a sentence. Finally, we merge the syntactic features with the semantic features from the BERT output layer in the feature fusion layer, obtaining a more accurate sentiment representation of the aspect. The experimental results on three public ABSA datasets show that using the syntactic feature extraction unit based on Star-Transformer to mine the syntactic knowledge in the middle layers of BERT can effectively improve the accuracy of sentiment classification. BERT-SFE achieves the best performance compared with existing models.