Tibetan sentiment classification method based on semi-supervised recursive autoencoders

Document Type

Article

Publication Date

1-1-2019

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.

Identifier

85075265842 (Scopus)

Publication Title

Computers Materials and Continua

External Full Text Location

https://doi.org/10.32604/cmc.2019.05157

e-ISSN

15462226

ISSN

15462218

First Page

707

Last Page

719

Issue

2

Volume

60

Grant

61503424

Fund Ref

National Natural Science Foundation of China

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