“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.”