Microblogging Short Text Classification Based on Word2Vec
Yonghui Zhang, Jingang Liu
Available Online April 2016.
- https://doi.org/10.2991/emim-16.2016.86How to use a DOI?
- Word2Vec;Features extension;Microblogging short text;SVM;Classification
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Yonghui Zhang AU - Jingang Liu PY - 2016/04 DA - 2016/04 TI - Microblogging Short Text Classification Based on Word2Vec BT - 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 - https://doi.org/10.2991/emim-16.2016.86 ID - Zhang2016/04 ER -