Enhancing Implicit Sentiment Learning via the Incorporation of Part-of-Speech for Aspect-based Sentiment Analysis

Wang Junlang, Li Xia, He Junyi, Zheng Yongqiang, Ma Junteng

Abstract
“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.”
Anthology ID:
2023.ccl-1.67
Volume:
Proceedings of the 22nd Chinese National Conference on Computational Linguistics
Month:
August
Year:
2023
Address:
Harbin, China
Editors:
Maosong Sun, Bing Qin, Xipeng Qiu, Jing Jiang, Xianpei Han
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
786–800
Language:
English
URL:
https://aclanthology.org/2023.ccl-1.67
DOI:
Bibkey:
Cite (ACL):
Wang Junlang, Li Xia, He Junyi, Zheng Yongqiang, and Ma Junteng. 2023. Enhancing Implicit Sentiment Learning via the Incorporation of Part-of-Speech for Aspect-based Sentiment Analysis. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 786–800, Harbin, China. Chinese Information Processing Society of China.
Cite (Informal):
Enhancing Implicit Sentiment Learning via the Incorporation of Part-of-Speech for Aspect-based Sentiment Analysis (Junlang et al., CCL 2023)
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