Microblogging Short Text Classification Based on Word2Vec

DOI

10.2991/emim-16.2016.86How to use a DOI?

Keywords

Word2Vec;Features extension;Microblogging short text;SVM;Classification

Abstract

For the sparse features of the microblogging text, the author proposes a method of microblogging text classification based on the features extension by Word2Vec. We train the text by using Word2Vec tool and find the words which are similar to original features semantic as the features of short text. Then we expand the features to the original text, and finally classify the subject of microblogging text by using SVM method. Experimental results show that the method has high accuracy recall and F1 values compared with the traditional method of vector space model and LDA topic model.

Copyright

© 2016, 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  - Yonghui Zhang
AU  - Jingang Liu
PY  - 2016/04
DA  - 2016/04
TI  - Microblogging Short Text Classification Based on Word2Vec
BT  - Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society
PB  - Atlantis Press
SP  - 395
EP  - 401
SN  - 2352-538X
UR  - https://doi.org/10.2991/emim-16.2016.86
DO  - 10.2991/emim-16.2016.86
ID  - Zhang2016/04
ER  -