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| Sentiment Analysis in Cross-Domain Environment with Deep Representative Learning |
Yu Chuanming1, Feng Bolin1, An Lu2( ) |
1 School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
2 School of Information Management, Wuhan University, Wuhan 430072, China |
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Abstract [Objective] The study trains the model with the source domain of rich labeling/tagging data and to project the source and target domain documents into the same feature space. This paper tries to solve the performance issue facing the target domain due to the lack of data. [Methods] First, we collected the Chinese, English and Japanese comments on books, DVDs and music from Amazon. Then, we proposed a Cross Domain Deep Representation Model (CDDRM) based on the Convolutional Neural Network (CNN) and Structural Correspondence Learning (SCL) techniques. Finally, we conducted cross-domain knowledge transfer and sentiment analysis. [Results] We found the best F value of CDDRM was 0.7368, which indicated the effectiveness of the proposed model. [Limitations] The F1 value of our model on long articles needs to be improved. [Conclusions] Transfer learning could help supervised learning obtain good classification results with small training sets. Compared with traditional methods, CDDRM does not require the training and testing sets having same or similar data structure.
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Received: 31 May 2017
Published: 26 July 2017
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| [1] |
Blitzer J, Dredze M, Pereira F.Domain Adaptation for Sentiment Classification[C]//Proceedings of Association for Computational Linguistics - ACL 2007.2007.
|
| [2] |
Denecke K.Are SentiWordNet Scores Suited for Multi- domain Sentiment Classification?[C]//Proceedings of International Conference on Digital Information Management. IEEE, 2009: 1-6.
|
| [3] |
Li F, Pan S J, Jin O, et al.Cross-domain Co-extraction of Sentiment and Topic Lexicons[C]// Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers. 2012: 410-419.
|
| [4] |
Bollegala D, Weir D, Carroll J.Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-domain Sentiment Classification[C]// Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2011: 132-141.
|
| [5] |
Glorot X, Bordes A, Bengio Y.Domain Adaptation for Large-scale Sentiment Classification: A Deep Learning Approach[C]// Proceedings of the 28th International Conference on Machine Learning. 2011: 513-520.
|
| [6] |
Ando R K, Zhang T.A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data[J]. Journal of Machine Learning Research, 2005, 6(3): 1817-1853.
doi: 10.1002/cem.976
|
| [7] |
Blitzer J, McDonald R, Pereira F. Domain Adaptation with Structural Correspondence Learning[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2006: 120-128.
|
| [8] |
Pan S J, Yang Q.A Survey on Transfer Learning[J]. IEEE Transactions on Knowledge & Data Engineering, 2010, 22(10): 1345-1359.
doi: 10.1109/TKDE.2009.191
|
| [9] |
Fernández A M, Esuli A, Sebastiani F.Distributional Correspondence Indexing for Cross-lingual and Cross-domain Sentiment Classification[J]. Journal of Artificial Intelligence Research, 2016, 55: 131-163.
doi: 10.1613/jair.4762
|
| [10] |
Kim Y.Convolutional Neural Networks for Sentence Classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 1746-1751.
|
| [11] |
Kalchbrenner N, Grefenstette E, Blunsom P.A Convolutional Neural Network for Modelling Sentences[OL]. arxiv PrePrint, arXiv: 1404.2188.
|
| [12] |
Collobert R, Weston J.A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning[C]//Proceedings of the 25th International Conference on Machine Learning.2008: 160-167.
|
| [13] |
Gao J, Pantel P, Gamon M, et al.Modeling Interestingness with Deep Neural Networks[C]// Proceedings of Conference on Empirical Methods in Natural Language Processing. 2014: 2-13.
|
| [14] |
Yan C, Zhang B, Coenen F.Driving Posture Recognition by Convolutional Neural Networks[C]// Proceedings of International Conference on Natural Computation. 2015: 680-685.
|
| [15] |
Ngiam J, Koh P, Chen Z, et al.Sparse Filtering[C]// Proceedings of the Neural Information Processing Systems Conference. 2011: 1125-1133.
|
| [16] |
Dahl G E, Ranzato M, Mohamed A R, et al.Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine.[C]// Proceedings of the Neural Information Processing Systems Conference, British Columbia, Canada. DBLP, 2010: 469-477.
|
| [17] |
Krizhevsky A, Sutskever I, Hinton G E.ImageNet Classification with Deep Convolutional Neural Networks[C]// Proceedings of International Conference on Neural Information Processing Systems. Curran Associates Inc, 2012: 1097-1105.
|
| [18] |
Boser B E, Guyon I M, Vapnik V N.A Training Algorithm for Optimal Margin Classifiers[C]// Proceedings of the 5th Annual Workshop on Computational Learning Theory. 1996: 144-152.
|
| [19] |
Tang Y.Deep Learning Using Linear Support Vector Machines [OL]. arxiv PrePrint, arXiv: 1306.0239.
|
| [20] |
Prettenhofer P, Stein B. Cross-Lingual Adaptation Using Structural Correspondence Learning[J]. ACM Transactions on Intelligent Systems & Technology, 2011, 3(1): Article No. 13.
doi: 10.1145/2036264.2036277
|
| [21] |
Prettenhofer P, Stein B.Webis-cls-10 Dataset [OL].
|
| [22] |
Duchi J, Hazan E, Singer Y.Adaptive Subgradient Methods for Online Learning and Stochastic Optimization[J]. Journal of Machine Learning Research, 2011, 12(7): 2121-2159.
doi: 10.1109/TNN.2011.2146788
|
| [23] |
Glorot X, Bengio Y.Understanding the Difficulty of Training Deep Feedforward Neural Networks[J]. Journal of Machine Learning Research, 2010, 9: 249-256.
|
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