  Domain adaptation tasks such as cross-domain sentiment classification aim to
utilize existing labeled data in the source domain and unlabeled or few labeled
data in the target domain to improve the performance in the target domain via
reducing the shift between the data distributions. Existing cross-domain
sentiment classification methods need to distinguish pivots, i.e., the
domain-shared sentiment words, and non-pivots, i.e., the domain-specific
sentiment words, for excellent adaptation performance. In this paper, we first
design a Category Attention Network (CAN), and then propose a model named
CAN-CNN to integrate CAN and a Convolutional Neural Network (CNN). On the one
hand, the model regards pivots and non-pivots as unified category attribute
words and can automatically capture them to improve the domain adaptation
performance; on the other hand, the model makes an attempt at interpretability
to learn the transferred category attribute words. Specifically, the
optimization objective of our model has three different components: 1) the
supervised classification loss; 2) the distributions loss of category feature
weights; 3) the domain invariance loss. Finally, the proposed model is
evaluated on three public sentiment analysis datasets and the results
demonstrate that CAN-CNN can outperform other various baseline methods.
