## 1 Introduction  
Much sentiment classification research focuses on training and testing classification models within a specific domain [1]. Transfer learning is a necessary technique to resolve the lack of labeled reviews in cross-domain product review sentiment classification. In many situations, plentiful product reviews from one domain (source domain) exist in which the sentiment is labeled but the application requires product review prediction from different, but related, domains (target domains) where the sentiment is unlabeled. For example, assume that the task is to build a book review classifier and that an insufficient number of labeled book reviews are available for training the classifier but that labeled electronics reviews are abundant. However, different domains have different feature spaces and distributions. In the book domain, words such as \"exciting\" and \"graphic novel\" are usually used to express a positive sentiment, whereas words such as \"boring\" or \"drowsy\" express a negative sentiment. In contrast, in the electronics domain, words such as \"durable\" and \"light\" are used to express a positive sentiment, whereas words such as \"expensive\" or \"short battery life\" often express a negative sentiment. Annotating reviews for a new domain is an expensive task that requires linguists skilled in natural language processing (NLP). Unlabeled reviews, however, are abundantly available. Nevertheless, when a sentiment classification model-trained source domain is directly applied to a different target domain, the result is often unsatisfactory because the bias between the source domain reviews and the target domain reviews hinders the learning of an accurate sentiment classification model. In such scenarios, transfer learning focuses on the challenge of disentangling this domain discrepancy. Researchers have proposed various methods to tackle this problem using non-deep neural networks [2; 3; 4; 5; 6; 7; 8]. However, these conventional approaches must extract customized features from the text and then feed the features into a classical shallow classifier such as a support vector machine (SVM). This process is empirical and task dependent, and it requires considerable engineering skills and domain expertise to construct features that are specific to a certain task. If a different classification task is addressed, this feature engineering method may not work well. However, such problems can be avoided if representative features can be automatically learned using a general-purpose learning procedure. This process is the key advantage of deep learning [9]. As a subfield of machine learning, deep learning aims to use a learning-based method to solve complex non-linear problems that normally rely on the processes and structure of the human brain. The deep learning approach can capture intermediate representations by constructing them hierarchically. Some research has been conducted to address various issues that text-fields face in deep neural networks. [PERSON] [10] used word vectors as input and trained a simple CNN with a single convolutional layer to perform sentence classification. The word vectors were pre-trained by Word2 Vec [11] on 100 billion words from Google News. The results suggested that pre-trained vectors are satisfactory and function as \"universal\" feature extractors. Fine-tuning the pre-trained vectors for each task produced further improvements. [PERSON] et al. [12] proposed a dynamic convolutional neural network that used dynamic K-max pooling to model sentences and introduced multiple feature maps that could capture short- or long-range semantic relations between words. This method did not rely on external sources, making the DCNN directly applicable to hard-to-parse sentences. For transfer learning, deep neural networks can generalize well from one domain to another. The internal representation of the neural network contains no discriminative information about the raw input [13].