Document Sentiment Classification based on the Word Embedding

DOI

10.2991/icmmcce-15.2015.92How to use a DOI?

Keywords

Word Embedding; Support Vector Machine; Sentiment Classification

Abstract

N-gram feature is commonly used to represent document, however, it often leads to the curse of dimensionality. Sentiment classification based on word embedding and SVM is proposed. The method uses word embedding to represent document, which can make the final representation of the document consistent with the dimension of word embedding. Experiments show that the proposed method can significant reduce the dimension of document representation and improve the accuracy of document sentiment classification.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yanping Yin
AU  - Zhong Jin
PY  - 2015/12
DA  - 2015/12
TI  - Document Sentiment Classification based on the Word Embedding
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SP  - 456
EP  - 461
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.92
DO  - 10.2991/icmmcce-15.2015.92
ID  - Yin2015/12
ER  -