Category-Level Adversarial Network for Cross-Domain Sentiment Classification

Cross-domain sentiment classification utilizes useful information in the source domain to improve the sentiment classification accuracy in the target domain which has few or no labeled data. Most existing methods based on single domain classifier only consider the global alignment without taking category-level alignment into consideration, which can lead to the mismatch of category-level features and reduce classification accuracy. To slove the above problem, we propose the Category-level Adversarial Network (CAN). On the basis of single domain classifier, CAN adds K category-wise domain classifiers which can achieve fine-grained alignment of different data distributions by combining the label information and document representations. Specifically, we obtain document representations by introducing transferable attention network which mirrors the hierarchical structure of documents and transfers attentions across domains. Experiments results demonstrate that CAN model outperforms state-of-the-art methods on the Amazon and Airline datasets.