Existing implicit sentiment learning methods mainly focus on capturing implicit sentiment knowledge individually, without paying more attention to the potential connection between implicit and explicit sentiment. From a linguistic perspective, implicit and explicit sentiment expressions are essentially similar when conveying the same sentiment polarity for a specific aspect. In this paper, we present an expression paraphrase strategy and a novel sentiment-consistent contrastive learning mechanism to learn the intrinsic connections between implicit and explicit sentiment expressions and integrate them into the model to enhance implicit sentiment learning. We perform extensive experiments on public datasets, and the results show the significant efficacy of our method on implicit sentiment analysis.

“Implicit sentiment modeling in aspect-based sentiment analysis is a challenging problem due tocomplex expressions and the lack of opinion words in sentences. Recent efforts focusing onimplicit sentiment in ABSA mostly leverage the dependency between aspects and pretrain onextra annotated corpora. We argue that linguistic knowledge can be incorporated into the modelto better learn implicit sentiment knowledge. In this paper, we propose a PLM-based, linguis-tically enhanced framework by incorporating Part-of-Speech (POS) for aspect-based sentimentanalysis. Specifically, we design an input template for PLMs that focuses on both aspect-relatedcontextualized features and POS-based linguistic features. By aligning with the representationsof the tokens and their POS sequences, the introduced knowledge is expected to guide the modelin learning implicit sentiment by capturing sentiment-related information. Moreover, we alsodesign an aspect-specific self-supervised contrastive learning strategy to optimize aspect-basedcontextualized representation construction and assist PLMs in concentrating on target aspects. Experimental results on public benchmarks show that our model can achieve competitive andstate-of-the-art performance without introducing extra annotated corpora.”