Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus
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...
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doaj-61b105d0bcea4a19a96fa65a4e3e6f782020-11-24T22:14:52ZengMDPI AGAlgorithms1999-48932017-07-011038110.3390/a10030081a10030081Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale CorpusXiaocong Wei0Hongfei Lin1Yuhai Yu2Liang Yang3School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaThe 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.https://www.mdpi.com/1999-4893/10/3/81cross-domainCNNsentiment classificationlarge-scaleproduct review |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaocong Wei Hongfei Lin Yuhai Yu Liang Yang |
spellingShingle |
Xiaocong Wei Hongfei Lin Yuhai Yu Liang Yang Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus Algorithms cross-domain CNN sentiment classification large-scale product review |
author_facet |
Xiaocong Wei Hongfei Lin Yuhai Yu Liang Yang |
author_sort |
Xiaocong Wei |
title |
Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus |
title_short |
Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus |
title_full |
Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus |
title_fullStr |
Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus |
title_full_unstemmed |
Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus |
title_sort |
low-resource cross-domain product review sentiment classification based on a cnn with an auxiliary large-scale corpus |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2017-07-01 |
description |
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. |
topic |
cross-domain CNN sentiment classification large-scale product review |
url |
https://www.mdpi.com/1999-4893/10/3/81 |
work_keys_str_mv |
AT xiaocongwei lowresourcecrossdomainproductreviewsentimentclassificationbasedonacnnwithanauxiliarylargescalecorpus AT hongfeilin lowresourcecrossdomainproductreviewsentimentclassificationbasedonacnnwithanauxiliarylargescalecorpus AT yuhaiyu lowresourcecrossdomainproductreviewsentimentclassificationbasedonacnnwithanauxiliarylargescalecorpus AT liangyang lowresourcecrossdomainproductreviewsentimentclassificationbasedonacnnwithanauxiliarylargescalecorpus |
_version_ |
1725796705725579264 |