KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis
This addresses the domain gap problem in sentiment analysis for applications requiring adaptation across different domains, representing an incremental advance.
The paper tackles cross-domain sentiment analysis by incorporating external commonsense knowledge from ConceptNet to enrich document semantics, resulting in improved performance over state-of-the-art methods.
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.