Modeling Inter-Aspect Relations With Clause and Contrastive Learning for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to identify the sentiment polarity of the given aspect. Recent studies fail to establish the relation among multiple aspects in one sentence. To address this issue, a clause-level relational graph attention network with contrastive learning (CLRCL) model is proposed. Specifically, the given sentence is segmented into clauses to obtain the relation between two aspects based on clause-level interaction. Then, to integrate multiple-aspect information, a clause-level relational graph which contains all aspects and inter-aspect relations is developed. Notably, to precisely learn the inter-aspect relations, the supervised contrastive learning strategy is used. Experimental results reveal that the proposed model is a competitive alternative compared with the state-of-the-art methods.