  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.
