TY  - EJOU
AU  - Yan, Xiaodong  
AU  - Song, Wei  
AU  - Zhao, Xiaobing  
AU  - Wang, Anti  

TI  - Tibetan Sentiment Classification Method Based on Semi-Supervised Recursive Autoencoders
T2  - Computers, Materials \& Continua

PY  - 2019
VL  - 60
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Recursive autoencoders (RAE)
KW  -  sentiment classification
KW  -  word vector

DO  - 10.32604/cmc.2019.05157