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Tibetan Sentiment Classification Method Based on Semi-Supervised Recursive Autoencoders

Xiaodong Yan1,2, Wei Song1,2,*, Xiaobing Zhao1,2, Anti Wang3

Minzu University of China, Beijing, 100081, China.
National Language Resource Monitoring & Research Center Minority Languages Branch, Beijing, China.
New Jersey Institute of Technology, Newark, NJ, 07102, USA.

* Corresponding Author: Wei Song. Email: email.

Computers, Materials & Continua 2019, 60(2), 707-719. https://doi.org/10.32604/cmc.2019.05157

Abstract

We apply the semi-supervised recursive autoencoders (RAE) model for the sentiment classification task of Tibetan short text, and we obtain a better classification effect. The input of the semi-supervised RAE model is the word vector. We crawled a large amount of Tibetan text from the Internet, got Tibetan word vectors by using Word2vec, and verified its validity through simple experiments. The values of parameter α and word vector dimension are important to the model effect. The experiment results indicate that when α is 0.3 and the word vector dimension is 60, the model works best. Our experiment also shows the effectiveness of the semi-supervised RAE model for Tibetan sentiment classification task and suggests the validity of the Tibetan word vectors we trained.

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Cite This Article

APA Style
Yan, X., Song, W., Zhao, X., Wang, A. (2019). Tibetan sentiment classification method based on semi-supervised recursive autoencoders. Computers, Materials & Continua, 60(2), 707-719. https://doi.org/10.32604/cmc.2019.05157
Vancouver Style
Yan X, Song W, Zhao X, Wang A. Tibetan sentiment classification method based on semi-supervised recursive autoencoders. Comput Mater Contin. 2019;60(2):707-719 https://doi.org/10.32604/cmc.2019.05157
IEEE Style
X. Yan, W. Song, X. Zhao, and A. Wang "Tibetan Sentiment Classification Method Based on Semi-Supervised Recursive Autoencoders," Comput. Mater. Contin., vol. 60, no. 2, pp. 707-719. 2019. https://doi.org/10.32604/cmc.2019.05157



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