Cross-domain sentiment analysis (CDSA) aims to predict the sentiment polarities of reviews in the target domain using a sentiment classifier learned from the source labeled domain. Most existing studies are dominant with adversarial learning methods and focus on learning domain-invariant sentiment representations in both the source and target domains. However, since sentiment-specific features are not explicitly decoupled, the model may confuse domain features with sentiment features, thus affecting its generalization ability on target domains. Unlike previous studies, in this paper, we tackle the CDSA task from the view of disentangled representation learning, which explicitly learns the disentangled representations of review, focusing in particular on sentiment and domain semantics. Specifically, we disentangle sentiment-specific and domain-specific features from the text representation of the review by two different linear transformations. Then, we introduce a straightforward disentangled loss to disallow the sentiment-specific feature to capture domain information. Moreover, we leverage target unlabeled data to improve the quality of the learned sentiment-specific features via prototypical learning. It indirectly encourages the sentiment-specific features of target samples having potentially different classes more discriminative. Extensive experiments on widely used CDSA datasets show that our method surpasses competitive baselines and achieves new state-of-the-art results, demonstrating its effectiveness and superiority.