Introduce More Characteristics of Samples into Cross-domain Sentiment Classification

Because of the discrepancy between different domains, the sentiment classifier trained in a source domain can't get a good performance in a target domain. Domain adaptation algorithms aim at solving such problems. One of the main algorithms aim at finding domain-invariable representations of inputs, which pays more attention to the common features of different domains and ignores the characteristics of samples themselves. In our paper, we propose a Fuzziness Based Domain-Adversarial Neural Network with Auto-Encoder (Fuzzy-DAAE). It not only uses a domain classifier to find domain-invariable features, but also uses an auto-encoder to reconstruct inputs to keep characteristics of samples. In order to introduce more supervised information of target samples, we also add unlabeled target samples and their predicted labels to the original training data according to their fuzziness and then retrain the whole model. Experiments on Amazon product reviews show that our proposed model has the best or comparative results compared with the existing models. It's worthwhile to notice that our model can be used in any other domain adaptation tasks, not limited to cross-domain sentiment classification.