Enhanced Syntactic and Semantic Graph Convolutional Network With Contrastive Learning for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of a given specific aspect in the sentence. Recent studies focus on leveraging graph convolutional neural networks to encode both syntactic and semantic information. However, current syntactic parsers, which are not specifically for ABSA, introduce noise to the syntactic information. Besides, ongoing studies ignore the distinctiveness of semantics and syntax. To address these issues, we proposed an enhanced syntactic and semantic graph convolutional network (GCN) with contrastive learning in this article. An aspect-oriented syntactic graph is constructed with aspect-specific perturbed masking for reducing the syntactic noise, and a semantic graph is established with self-attention weights from bidirectional encoder representation from transformers (BERT). The semantic and syntactic representations are further enhanced by both sentiment polarity-based supervised contrastive learning and syntactic reliability-based unsupervised contrastive learning. Furthermore, label embeddings of syntactic reliability are learned to determine the weights of syntactic and semantic information. Extensive experiments on four publicly available datasets demonstrate that our model is more competitive than the state-of-the-arts.