## 5 Experimental Evaluation
### Experimental Configuration
The results confirm that the pre-trained vectors are both satisfactory and \"universal\" feature extractors that are beneficial for cross-domain classification. Second, through the convolutional and max pooling operations, LM-CNN-NLB can effectively use local contextual features and global contextual features to capture the generic variations present in all factors that are suitable for cross-domain classification. LM-CNN-NLB behaves better than previous baselines on most tasks, while LM-CNN-LB achieves the best performance. When training \(S_{cnn}\) on the source domain, it extracts no discriminative feature representation, therefore, the features can be shared across the source domain and target domain. Fine-tuning the parameters transferred from \(S_{cnn}\) on a small amount of target domain data causes the \(T_{cnn}\) to learn a feature specific to the target domain task. Comparing LM-CNN-BB with LM-CNN-LB, when the available corpus is small, it is inappropriate for training parameter-rich models such as neural networks. However, introducing a large-scale similar auxiliary dataset effectively expands the corpus. By transferring parameters from the convolutional neural network trained on the source domain to initialize another identical neural architecture and then fine-tuning with small amounts of target domain data, the target network can learn a feature representation that generalizes well across different domains. **Corpus size and multi-source domain**: Figure 3 shows the effect of corpus size and a multi-source domain. For a multi-source domain, one domain was used as the target domain, and the other three domains were used as the source domain. The three source domains were related but were different from the target domain. For computational reasons, each domain dataset consisted of 20,000 reviews extracted from \(D_{l}\) and 2000 from \(D_{b}\). For example, the \(S_{cnn}\) was trained on source domain reviews consisting of the following three domains: \(B_{D_{bl}}\), \(D_{D_{bl}}\) and \(E_{D_{bl}}\). To train the \(S_{cnn}\), the source domain dataset was split into a training set and a validation set. The training set included 54,000 reviews (90% of each domain extracted from \(D_{l}\)) and a validation set of 12,000 reviews. The \(T_{cnn}\) was trained on the target domain \(K_{D_{l}}\) using a training set size of 700 and a validation set size of 300, totalling 5% of \(K_{D_{l}}\). The \(T_{cnn}\) model can then be used to predict \(K_{D_{b}}\) (denoted as the multi-source domain in Figure 3). The multi-source domain was also compared with LM-CNN-LB on different corpus sizes. LM-CNN-LB62k andLM-CNN-LB22k were the datasets for each domain consisting of 60,000 data points, 20,000 reviews extracted from \(D_{l}\) and 2000 from \(D_{b}\). The source domain dataset for training the \(S_{cnn}\) from \(D_{bl}\) was split into a training set and a validation set. The training set was 90% of each domain extracted from \(D_{l}\). The validation set consisted of the remaining reviews from each domain extracted from \(D_{l}\) plus 2000 from \(D_{b}\). To train \(T_{cnn}\) on the target domain, the training set and the validation set totaled 5% of each domain extracted from \(D_{l}\). The compositions of the corpuses are displayed in detail in Table 4. The accuracy of the target domain dataset is the average score of the other domains used for prediction. For example, for LM-CNN-LB62k, accuracy of D\(\rightarrow\)B, E\(\rightarrow\)B, K\(\rightarrow\)B is \(A\mathit{c}ur_{D}\), \(A\mathit{c}ur_{E}\) and \(A\mathit{c}ur_{K}\), respectively, while the average classification accuracy for Books is \((A\mathit{c}ur_{D}+A\mathit{c}ur_{E}+A\mathit{c}ur_{K})/3\). The embedding layer weight of the above methods is a 300-dimension word vector pre-trained on the self-built corpus described in Section 5.2. Figure 2: Comparison of different methods on the benchmark dataset for cross-domain sentiment classification. As shown in Figure 3, the proposed method performed better using the larger corpus.