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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: .

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.

Keywords

Recursive autoencoders (RAE), sentiment classification, word vector

Cite This Article

X. Yan, W. Song, X. Zhao and A. Wang, "Tibetan sentiment classification method based on semi-supervised recursive autoencoders," Computers, Materials & Continua, vol. 60, no.2, pp. 707–719, 2019.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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