Multi-source domain adaptation with joint learning for cross-domain sentiment classification

Abstract

Cross-domain sentiment classification uses knowledge from source domain tasks to enhance the sentiment classification of the target task. It can reduce the workload of data annotations in the new domain, and significantly improve the utilization of labeled resources in the source domains. Available approaches generally use knowledge from a single-source domain and hard parameter sharing methods, which are likely to ignore the differences among domain-specific features. We propose a novel framework with multi-source domain adaptation and joint learning for multi-source cross-domain sentiment classification tasks This framework uses bi-directional gated recurrent units and convolutional neural networks for deep feature extraction and soft parameter sharing for information transfer across tasks. Furthermore, it minimizes distance constraints for deep domain fusion. Multi-source domain adaptation involves multiple concurrent task learning, and the gradients are simultaneously back propagated. We validate the proposed framework on multi-source cross-domain sentiment classification datasets in Chinese and English. The experimental results demonstrate that the proposed method is more effective than state-of-the-art methods in improving accuracy and generalization capability.