Cross-domain sentiment analysis has received significant attention in recent years, prompted by the need to combat the domain gap between different applications that make use of sentiment analysis. In this paper, we take a novel perspective on this task by exploring the role of external commonsense knowledge. We introduce a new framework, KinGDOM, which utilizes the ConceptNet knowledge graph to enrich the semantics of a document by providing both domain-specific and domain-general background concepts. These concepts are learned by training a graph convolutional autoencoder that leverages inter-domain concepts in a domain-invariant manner. Conditioning a popular domain-adversarial baseline method with these learned concepts helps improve its performance over state-of-the-art approaches, demonstrating the efficacy of our proposed framework.

本文提出了一种新的框架KinGDOM，利用ConceptNet知识图谱中的通用知识和领域特定知识来丰富文档的语义，通过训练图卷积自编码器来学习交叉领域知识，同时利用外部常识知识来改进领域敌对基准方法的性能。它在交叉领域情感分析任务中展现出比其他最先进的方法更好的性能。

KinGDOM：基于知识引导的领域自适应情感分析