## 3 Data Collection  
**Cross-domain product review transfer learning benchmark (\(D_{b}\))**: A transfer learning benchmark defined as described by [PERSON] et al. (2016) has been widely used in many cross-domain sentiment classification methods. It contains Amazon product reviews consisting of the following four different product types: books (\(B_{D_{b}}\)), DVDs (\(D_{D_{b}}\)), electronics (\(E_{D_{b}}\)) and kitchen appliances (\(K_{D_{b}}\)). There are 1000 positive reviews and 1000 negative reviews for each domain. In this dataset, 12 pairs of cross-domain sentiment classification tasks are constructed as follows: \(D_{D_{b}}\!\rightarrow\!B_{D_{b}}\), \(E_{D_{b}}\!\rightarrow\!B_{D_{b}}\), \(K_{D_{b}}\!\rightarrow\!B_{D_{b}}\), \(K_{D_{b}}\!\rightarrow\!E_{D_{b}}\), \(D_{D_{b}}\!\rightarrow\!E_{D_{b}}\), \(B_{D_{b}}\!\rightarrow\!D_{D_{b}}\), \(K_{D_{b}}\!\rightarrow\!D_{D_{b}}\), \(E_{D_{b}}\!\rightarrow\!D_{D_{b}}\), \(B_{D_{b}}\!\rightarrow\!K_{D_{b}}\), \(D_{D_{b}}\!\rightarrow\!K_{D_{b}}\), and \(E_{D_{b}}\!\rightarrow\!K_{D_{b}}\), where the word before an arrow corresponds to the source domain and the word after an arrow corresponds to the target domain. Due to its small size, \(D_{b}\) is typically confined to conventional non-deep neural network methods and has been considered unsuitable for training parameter-rich neural networks. **Amazon product review dataset (\(D_{l}\))**: To evaluate the ability of the deep neural network to perform cross-domain product review sentiment classification, a large-scale Amazon product review dataset collected by [PERSON] et al. (2017) is introduced to supplement \(D_{b}\). This dataset contains product reviews and scores from 24 product categories sold on Amazon.com, including 142.8 million reviews spanning from May 1996 to July 2014. Review scores lie on an integer scale from 1 to 5. Reviews with ratings of 1 and 2 are viewed as negative, and reviews with ratings of 4 and 5 are considered positive. For this study, the following four categories of original product reviews were extracted for testing purposes: Books (\(B_{D_{l}}\)), Movies and TV (\(D_{D_{l}}\)), Electronics (\(E_{D_{l}}\)) and Home and Kitchen (\(K_{D_{l}}\)) from the most recent year (2014). In total, 50,000 positive reviews and 50,000 negative reviews were used from each domain. Such a large-scale dataset can effectively reflect the transferability of a deep neural network. **Auxiliary product review dataset (\(D_{b}\))**: KL (Kullback-Leibler) divergence is a method for measuring the similarity between two probability distributions and can be applied to measure the similarity between domains. Table 1 displays the KL divergence of the corresponding domain between \(D_{b}\) and \(D_{l}\), while Table 2 shows the KL divergence between the different domains of \(D_{b}\) and \(D_{l}\). In Table 2, the second and third columns show the KL divergence between the different domains in \(D_{b}\) and \(D_{l}\), respectively. The fourth column is the KL divergence between the domains (the source domain is \(D_{l}\) and the target domain is \(D_{b}\)). Comparing Table 1 with Table 2, the KL divergence of the corresponding domains between \(D_{b}\) and \(D_{l}\) is far less than between the different domains in \(D_{b}\) or \(D_{l}\). For example, the similarity of \(B_{D_{b}}\) and \(B_{D_{l}}\) is far higher than that of \(E_{D_{b}}\!\rightarrow\!B_{D_{b}}\) or \(E_{D_{l}}\!\rightarrow\!B_{D_{l}}\). Thus, a similar auxiliary corpus (denoted as \(D_{b}\)) can be constructed for each domain (see Table 3) to supplement the small-size benchmark \(D_{b}\). The \(D_{b_{l}}\) corpus is sufficiently large to train parameter-rich models such as deep neural networks; thus, the use of deep neural networks is expected to succeed in terms of cross-domain product review sentiment classification and its evaluation on the benchmark dataset.