  Cross-domain sentiment analysis aims to predict the sentiment of texts in the
target domain using the model trained on the source domain to cope with the
scarcity of labeled data. Previous studies are mostly cross-entropy-based
methods for the task, which suffer from instability and poor generalization. In
this paper, we explore contrastive learning on the cross-domain sentiment
analysis task. We propose a modified contrastive objective with in-batch
negative samples so that the sentence representations from the same class will
be pushed close while those from the different classes become further apart in
the latent space. Experiments on two widely used datasets show that our model
can achieve state-of-the-art performance in both cross-domain and multi-domain
sentiment analysis tasks. Meanwhile, visualizations demonstrate the
effectiveness of transferring knowledge learned in the source domain to the
target domain and the adversarial test verifies the robustness of our model.
