Tibetan Sentiment Classification Method Based on Semi-Supervised Recursive Autoencoders

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 T...

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Veröffentlicht in:Computers, materials & continua Jg. 60; H. 2; S. 707 - 719
Hauptverfasser: Yan, Xiaodong, Song, Wei, Zhao, Xiaobing, Wang, Anti
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Henderson Tech Science Press 2019
Schlagworte:
Classification
Recursive methods
Sentiment analysis
ISSN:1546-2226, 1546-2218, 1546-2226
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2019.05157