Enhancing Implicit Sentiment Learning via the Incorporation of Part-of-Speech for Aspect-based Sentiment Analysis (2023)

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.”

## introduction aspect-based sentiment analysis (absa) aims to identify the sentiment polarities towards specific aspects in sentences. for example, in the sentence "the dessert is incredible but the service is terrible," the sentiment polarities towards the aspects "dessert" and "service" are positive and negative respectively. previous work on aspect-based sentiment analysis has focused on explicit sentiment expression for specific aspect terms. it means that the sentiment polarities towards the aspects can be explicitly revealed by opinion words. e.g., the sentence "the dessert is incredible" contains the opinion word "incredible" which carries the positive sentiment towards the corresponding aspect "dessert". many studies have been proposed and achieved promising results towards this task, such as attention mechanism-based methods @xcite @xcite , graph neural network-based methods @xcite @xcite , and pre-trained language model-based methods @xcite @xcite . however, due to the diversity and flexibility of natural language, sentences containing implicit sentiment expressions are common in human speech. for implicit sentiment, we refer to the recognition of subjective textual units where no polarity markers, opinion words or obvious descriptions are present but people are still able to state whether the text portion under analysis expresses the sentiment @xcite . as shown in ## conclusion in this paper, we propose a plm-based linguistically enhanced framework for aspect-based sentiment analysis based on the analysis of the feasibility of incorporating part-of-speech into the absa task. using pos-based linguistic knowledge, our method optimizes the plms' fine-tuning for implicit sentiment capturing. aspect-specific self-supervised contrastive learning allows the model to concentrate on target aspects when handling sentences containing multiple aspect terms. extensive experiments show that our proposed model can achieve competitive and state-of-the-art performance relative to baseline models without introducing extra corpora. although the introduction of pos as linguistic knowledge can effectively improve the enhancement of implicit sentiment detection in absa, there are still limitations. if there are difficulties in deriving precise pos sequences in low-resource settings, the pos-based solution might not provide sufficient information. further research can investigate approaches for integrating various linguistic knowledge into models for learning implicit sentiment without external sources.