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Algorithms 2017, 10(3), 81; doi:10.3390/a10030081

Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus

1
School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
2
School of Software Engineering, Dalian University of Foreign Languages, Dalian 116044, China
*
Author to whom correspondence should be addressed.
Received: 31 May 2017 / Revised: 12 July 2017 / Accepted: 18 July 2017 / Published: 19 July 2017
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Abstract

The literature [-5]contains several reports evaluating the abilities of deep neural networks in text transfer learning. To our knowledge, however, there have been few efforts to fully realize the potential of deep neural networks in cross-domain product review sentiment classification. In this paper, we propose a two-layer convolutional neural network (CNN) for cross-domain product review sentiment classification (LM-CNN-LB). Transfer learning research into product review sentiment classification based on deep neural networks has been limited by the lack of a large-scale corpus; we sought to remedy this problem using a large-scale auxiliary cross-domain dataset collected from Amazon product reviews. Our proposed framework exhibits the dramatic transferability of deep neural networks for cross-domain product review sentiment classification and achieves state-of-the-art performance. The framework also outperforms complex engineered features used with a non-deep neural network method. The experiments demonstrate that introducing large-scale data from similar domains is an effective way to resolve the lack of training data. The LM-CNN-LB trained on the multi-source related domain dataset outperformed the one trained on a single similar domain. View Full-Text
Keywords: cross-domain; CNN; sentiment classification; large-scale; product review cross-domain; CNN; sentiment classification; large-scale; product review
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Wei, X.; Lin, H.; Yu, Y.; Yang, L. Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus. Algorithms 2017, 10, 81.

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