Open Access

ARTICLE

Tibetan Sentiment Classification Method Based on Semi-Supervised Recursive Autoencoders

Xiaodong Yan^1,2, Wei Song^1,2,*, Xiaobing Zhao^1,2, Anti Wang³

1 Minzu University of China, Beijing, 100081, China.
2 National Language Resource Monitoring & Research Center Minority Languages Branch, Beijing, China.
3 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.

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