@Article{cmc.2019.05157,
AUTHOR = {Xiaodong  Yan, Wei  Song, Xiaobing  Zhao, Anti  Wang},
TITLE = {Tibetan Sentiment Classification Method Based on Semi-Supervised Recursive Autoencoders},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {60},
YEAR = {2019},
NUMBER = {2},
PAGES = {707--719},
URL = {http://www.techscience.com/cmc/v60n2/23058},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2019.05157}
}