## 5 Experimental Evaluation
### Experimental Configuration  
The weight of the embedding layer in LM-CNN-LB was initialized with 300-dimensional word vectors. These vectors were pre-trained with 100 billion words from Google News or a self-built corpus by Google Word2 Vec [[https://code.google.com/p/word2](https://code.google.com/p/word2) vec/]([https://code.google.com/p/word2](https://code.google.com/p/word2) vec/). The training parameters are as follows: cbow is 0, size is 200, window is 8, negative is 25, hs is 0, sample is 1e-4, threads is 20, binary is 1 and iter is 15. For the self-built corpus, reviews from four domain, Books, Movies and TV, Electronics and Home and Kitchen were extracted from a large Amazon product review dataset covering the year 2014 (collected by [PERSON] et al. (2014)). Neutral reviews (score rating 0) were removed, all the characters were converted to lower case, and punctuation was removed. The corpus contained 6.96 million product reviews, 386 million words and a vocabulary of 2.31 million words. Similarly, when preprocessing \(D_{b}\) and \(D_{bl}\), punctuation was removed, and all characters were converted to lower case. For computational reasons, the review input lengths were normalized to 100. Accuracy was used as an evaluation metric, and the experiment was implemented as described by Keras 2.0 Keras (2016). The programming language is Python and with NLTK natural language toolkit. The batch size of the neural network was 128. The number of convolution filters was 250, the filter width was 5, the convolution layer border mode was'same', and the activation function was _relu_. The optimizer was _RMSProp_, the max pooling length was 2, and all dropouts were 0.1. The activation function of the first fully connected layer was _relu_, and the number of hidden units was 250. The activation function of the second fully connected layer was _sigmoid_, and the number of hidden units was 1. Shuffling occurred after every epoch. The training procedure periodically evaluated the binary cross-entropy objective function on the training set and the validation set. The test performance was associated with the last epoch validation accuracy. The learning rates were set to 0.0005. The \(S_{cnn}\) network was trained with 15 epochs, and the \(T_{cnn}\) network was trained with 50 epochs. Our networks are trained on one NVIDIA Tesla K20c GPU in a 64-bit Dell computer with two 2.40 GHz CPUs, 64 G main memories in Dalian, China, and Ubuntu 12.04. The runtime of training our framework (such as LM-CNN-BB) is: one epoch requires 123 s when training \(S_{cnn}\) on the kitchen appliances domain and 15 s when training \(T_{cnn}\) on the electronics domain. **Comparison results and discussion**: Figure 2 shows the accuracy scores of the different methods for all pairs of tasks. The second set of bars shows that SVM-NLB performed better than SVM-NBB due to the introduction of the large-scale source domain. The large-scale similar auxiliary corpus was beneficial for cross-domain product sentiment classification tasks. Expanding the source domain data introduces more useful knowledge, which is the advantageous factor for cross-domain classification, is introduced. However, SVM-NLB still performs worse than methods that applied the transfer learning method, such as SCL-MI, SFA and SS-FE. Although SVM-NLB can learn more knowledge from large-scale data for cross-domain classification tasks, this algorithm is insufficient to bridge the gap between different domains. However, when LM-CNN-NLB is applied to the same corpus as SVM-NLB, the performance improves greatly. This improvement can be attributed primarily to the following two factors: first, as is well-known, word embedding is more effective than BOW. BOW is unable to capture the complex linguistic phenomena of words that are available in the word embedding representation, in which semantically close words are likewise close in the lower-dimensional vector space. Thus, word embedding contains more semantic information.