The literature contains several reports evaluating the abilities of deep neural networks in\ntext transfer learning. To our knowledge, however, there have been few efforts to fully realize the\npotential of deep neural networks in cross-domain product review sentiment classification. In this\npaper, we propose a two-layer convolutional neural network (CNN) for cross-domain product review\nsentiment classification (LM-CNN-LB). Transfer learning research into product review sentiment\nclassification based on deep neural networks has been limited by the lack of a large-scale corpus;\nwe sought to remedy this problem using a large-scale auxiliary cross-domain dataset collected from\nAmazon product reviews. Our proposed framework exhibits the dramatic transferability of deep\nneural networks for cross-domain product review sentiment classification and achieves state-of-the-art\nperformance. The framework also outperforms complex engineered features used with a non-deep\nneural network method. The experiments demonstrate that introducing large-scale data from similar\ndomains is an effective way to resolve the lack of training data. The LM-CNN-LB trained on the\nmulti-source related domain dataset outperformed the one trained on a single similar domain.
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