## 1 Introduction
The transfer learning ability of a deep neural network in the image field has been evaluated [14; 15]. [PERSON] et al. [14] proposed a deep learning layer based on a feature transference approach for image classification. By transferring low-, middle- and high-layer features in unsupervised or supervised ways, using lower-layer features trained in a supervised fashion in the case of CNNs and unsupervised features trained in the case of SDAs, this method achieved superior performance. [PERSON] et al. [15] quantified the transferability of each layer in an 8-layer convolutional neural network. For text, [PERSON] et al. [16] proposed a multi-layer transfer learning method based on non-negative matrix tri-factorization. This method used supplementary latent factors to improve the transfer learning performance. It also built every layer by including common and specific latent feature spaces to reduce negative transfer. [PERSON] et al. [17] proposed a framework capable of multi-task transfer learning. [PERSON] et al. [18] proposed a convolutional neural network method for mining user consumption intention (CIMM) on a child and baby corpus (source domain). This domain-adaptive framework was also effective for identifying user consumption intention in the movie domain (target domain). Studies have also focused on learning satisfactory representations via deep architecture to reduce transfer loss (improve the transfer ratio). [PERSON] et al. [19] proposed a deep learning method to learn an effective representation for domain adaptation based on stacked denoising auto-encoders. [PERSON] [20] proposed several challenging problems for transfer learning faces and investigated how to employ deep learning to resolve them. [PERSON] et al. [21] found that models that interleaved different layer-wise representation learning algorithms performed well. Contractive auto-encoders, denoising auto-encoders and spike-and-slab RBMs worked best on dense datasets, and sparse rectifier denoising auto-encoders worked best on sparse datasets. [PERSON] et al. [22] applied domain supervision and sentiment supervision to representation learning to address domain adaptation. By introducing domain labels and sentiment labels for loss functions based on KL divergence, the model could learn a more accurate domain-specialized and sentiment-specialized representation. The combination enabled the representation to be more domain-specific and sentiment-oriented and demonstrated that domain supervision is more effective than sentiment supervision. [PERSON] et al. [23] proposed a representation learning method by aligning the distributions of features across domains via standard back-propagation training. This method can be applied to both shallow and deep feed-forward architectures. To measure the transferability of neural network, similar to [15], which involved transfer learning in image analysis, [PERSON] et al. [24] studied the transferability of semantic-relative and semantic-irrelative tasks, layers and parameter initialization, multi-task learning and their combinations on three datasets. Other previous studies based on deep neural networks have focused on consumption intention [18], learning satisfactory representations [19; 20; 21; 22; 23], verifying the transferability of deep neural networks [24], multi-task transfer learning [17], online transfer learning [25]. Although there have been efforts to address product review text, such as [16], the neural network architecture and transfer schema used in this study are different from those presented in [16], and research on transferability still needs to be completed. No work has been conducted to fully realize the potential of deep neural networks in cross-domain product review sentiment classification, probably because existing product reviews for transfer learning have fewer resources. Transfer learning research of product review sentiment classification in deep neural networks has been severely limited by the lack of large-scale resources.