An improved unified domain adversarial category-wise alignment network for unsupervised cross-domain sentiment classification

Abstract

Cross-domain sentiment analysis (CDSA) aims to learn transferable knowledge from the source domain to facilitate the sentiment polarity classification on the target domain of lacking labeled data. Currently, two types of unsupervised domain adaptation (UDA) methods are widely used in CDSA tasks. One employs the domain adversarial strategy to extract domain-invariant features, and the other utilizes the distance metric strategy to reduce domain distribution discrepancy. However, the fine-grained domain-specific information related to categories aligned between domains is not preserved, which suppresses the performance of target-domain classification. To overcome the mentioned problem, a unified Domain Adversarial Category-wise Alignment Network (DACAN) was proposed in this paper. An integrated network was constructed with progressive multi-level feature learning. Specifically, a feature extraction module was constructed with parameter sharing between two domains at low-level text feature extraction layers. The domain adversarial module was added to enable shared knowledge transfer by extracting domain-invariant information and by updating the shared parameters at the feature extraction layers. A category-wise alignment module was built to achieve local distribution alignment at the high dimension-level semantic layers guided by fine-grained category structure information. Meanwhile, joint constraint was established with domain-invariant constraint based on domain adversarial, and domain-consistency constraint based on category-wise alignment. Comprehensive experiments were conducted on two standard Amazon review datasets. The results show that DACAN outperforms other state-of-the-art UDA methods by 0.7% and 1.1% on the two- and three-category CDSA tasks, respectively. Also, better performance results are achieved with a synergistic UDA scheme compared with a single UDA scheme.