Span-based relational graph transformer network for aspect–opinion pair extraction - Li, You; Wang, Chaoqiang; Lin, Yuming; Lin, Yongdong; Chang, Liang (Knowledge and information systems, 2022)
Aspect extraction and opinion extraction are two fundamental subtasks in aspect-based sentiment analysis. Many methods extract aspect terms or opinion terms but ignore the relationships between them. However, such relationships are crucial for downstream tasks, such as sentiment classification and commodity recommendation. Recently, methods have been proposed to extract both terms jointly; however, they fail to extract them as pairs. In this paper, we explore the aspect–opinion pair extraction task that aims to extract aspect and opinion terms in pairs. To carry out this task, we propose a span-based relational graph transformer network that consists of a span generator, a span classifier, and a relation detector. The span generator enumerates all possible spans to generate the candidates for aspect or opinion terms and filters non-aspects or non-opinions terms, while the relation classifier extracts aspect–opinion pairs. We propose a relational graph convolutional network to capture the dependent relationships between aspect and opinion terms. Extensive experiments show that the proposed model achieves the state-of-the-art performance using four benchmark datasets.