Cross-Domain Aspect-Based Sentiment Classification With Tripartite Graph Modeling

Previous studies on cross-domain aspect-based sentiment classification depend on the pivot features or utilize the target data for representation learning, which ignores the correlations between instances and words. In this study, we employ two strategies to connect different domains through tripartite graphs. Firstly, we employ a
word-topic-instance tripartite graph
to bridge the gap between different domains with the cross-domain topic distribution. The cross-domain topic distribution is learned by a neural topic model on different domains. Secondly, we use a
word-pivot-instance tripartite graph
to connect instances and words in different domains. The pivot clauses are generated from the instances, and are domain-independent and sentimentally aligned with the original text. Afterward, we employ graph convolutional networks to model over these tripartite graphs for cross-domain sentiment classification respectively. The experimental result shows that our model with tripartite graphs outperforms several competitive models. The result also indicates the effectiveness of the proposed tripartite graphs for cross-domain aspect-based sentiment classification.