Dependency Graph Convolution and POS Tagging Transferring for Aspect-Based Sentiment Classification

Aspect-based sentiment classification (ABSC) task is a fine-grained task in natural language processing, which mainly recognizes the sentiment polarity of various aspects in a sentence. Most of the existing work ignores the syntactic constraints of the local context, and few studies use feature enhancement when dealing with ABSC problems. To solve these problems, this paper proposes a new transfer learning model based on aspect sentiment analysis, namely LCF-TDGCN. It is based on local context focus mechanism and self-attention mechanism, and uses Part-Of-Speech (POS) tagging as an auxiliary task to enhance sentiment polarity. Secondly, this method utilizes the dependency graph convolution (DGC) to analyze the syntactic constraints of local context and capture long-term word dependencies. In addition, this paper integrates the pre-trained BERT model, and improves the performance of ABSC tasks by using syntactic information and word dependence. The experimental results on five different datasets show that the LCF-TDGCN produces good results.