## 5 Experimental Evaluation
### Large-Scale Corpus Experiments  
The transferability was exhibited on the large-scale dataset \(D_{l}\), as shown in Figure 5. SVM-NLL was the same as SVM-NLB, but the target domain test set was from \(D_{l}\). LM-CNN-LL22k, LM-CNN-LL62k, and LM-CNN-LL were the same as LM-CNN-LB22k, LM-CNN-62k and LM-CNN-LB, but the \(T_{cnn}\) model was used to test the reviews from \(D_{l}\) (excluding those used for training and validation). The results shown in Figure 5 indicate that the proposed method performed better than SVM-NLL. A larger corpus results in higher accuracy; this result is consistent with experiments on the benchmark \(D_{b}\).  
**Evaluating transfer readiness**: In this part, the transferability of LM-CNN-LL62k is evaluated by freezing different layers of \(T_{cnn}\), as shown in Table 5. For example, layer-0 of \(T_{cnn}\) was frozen after transferring parameters from \(S_{cnn}\) to \(T_{cnn}\). The parameters of layer-0 were constant during training. The parameters of the remaining layers (layer-1, layer-2, layer-3, and layer-4) were trained and fine-tuned using small amounts of labeled reviews from the target domain. The frozen layer/s of \(T_{cnn}\) are listed in the first column of Table 5, and the test accuracy is listed in the second column.  
Figure 4: Comparison of word embedding pre-trained on Google News and the proposed corpus.  
Figure 5: Comparison of different methods and corpus size on \(D_{l}\) for cross-domain sentiment classification.  
Fine-tuning more layers stimulates learning features that are specific to the target domain. The more layers that participate in fine-tuning, the more specific the features are to the target domain.  
Moreover, we analyzed the effect of epoch number when training the \(S_{cnn}\). As illustrated in Figure 6, the accuracy increases from epochs 1 to 4. Thus, a larger number of epochs does not necessarily lead to higher performance, suggesting that well-trained parameters from the source domain are crucial for this work. The selected number of epochs causes the \(S_{cnn}\) to learn discriminative feature representation that initializes \(T_{cnn}\) well. However, if the source domain is excessively trained, the parameters transferred from the \(S_{cnn}\) to the \(T_{cnn}\) will be overfit to the source domain. The learned feature representation of the \(S_{cnn}\) is then excessively specific to the source domain but will be underfit to the target domain.  
**Labeled target domain data**: We also tested the effect of using different proportions of the labeled target domain data, which varied from \(1/120\) to \(1/5\) for every domain of LM-CNN-LL62k. Figure 7 indicates that a larger number of labeled target domain data results in higher test accuracy in every task. It is not surprising that LM-CNN-LL62k can learn more information specific to the target domain from the extensive labeled data. The labeled data of the target domain is crucial for learning an effective feature representation for cross-domain sentiment classification.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frozen Layer/s** & **Accuracy** \\ \hline
0,1,2,3 & 88.12 \\
0,1,2 & 88.13 \\
0,1 & 88.66 \\
0 & 89.42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Main results of LM-CNN-LL62k for freezing different layers.  
Figure 6: Effect of \(S_{cnn}\) epoch number.