Enhanced Implicit Sentiment Understanding With Prototype Learning and Demonstration for Aspect-Based Sentiment Analysis

Abstract

In the field of social computing, the task of aspect-based sentiment analysis (ABSA) aims to classify the sentiment polarity of a given aspect in a sentence. The absence of explicit opinion words in the implicit aspect sentiment expressions poses a greater challenge for capturing their sentiment features in the reviews from social media. Many recent efforts use dependency trees or attention mechanisms to model the association between the aspect and other contextual words. However, dependency tree-based methods are inefficient in constructing valuable associations for sentiment classification due to the lack of explicit opinion words. In addition, the use of attention mechanisms to obtain global semantic information easily leads to an undesired focus on irrelevant words that may have sentiments but are not directly related to the specific aspect. In this article, we propose a novel prototype-based demonstration (PD) model for the ABSA task, which contains prototype learning and PD stages. In the prototype learning stage, we employ mask-aware attention to capture the global sentiment feature of aspect and learn sentiment prototypes through contrastive learning. This allows us to acquire comprehensive central semantics of the sentiment polarity that contains the implicit sentiment features. In the PD stage, to provide explicit guidance for the latent knowledge within the T5 model, we utilize prototypes similar to the aspect sentiment as the neural demonstration. Our model outperforms others with a 1.68%/0.28% accuracy gain on the Laptop/Restaurant datasets, especially in the ISE slice, showing improvements of 1.17%/0.26%. These results confirm the superiority of our PD-ABSA in capturing implicit sentiment and improving classification performance. This provides a solution for implicit sentiment classification in social computing.