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Domain adaptation tasks such as cross-domain sentiment classification aim toutilize existing labeled data in the source domain and unlabeled or few labeleddata in the target domain to improve the performance in the target domain viareducing the shift between the data distributions. Existing cross-domainsentiment classification methods need to distinguish pivots, i.e., thedomain-shared sentiment words, and non-pivots, i.e., the domain-specificsentiment words, for excellent adaptation performance. In this paper, we firstdesign a Category Attention Network (CAN), and then propose a model namedCAN-CNN to integrate CAN and a Convolutional Neural Network (CNN). On the onehand, the model regards pivots and non-pivots as unified category attributewords and can automatically capture them to improve the domain adaptationperformance; on the other hand, the model makes an attempt at interpretabilityto learn the transferred category attribute words. Specifically, theoptimization objective of our model has three different components: 1) thesupervised classification loss; 2) the distributions loss of category featureweights; 3) the domain invariance loss. Finally, the proposed model isevaluated on three public sentiment analysis datasets and the resultsdemonstrate that CAN-CNN can outperform other various baseline methods.

Domain Adaptation with Category Attention Network for Deep Sentiment Analysis