Microblogging Short Text Classification based on Word2Vec

被引：0

作者：

Zhang, Yonghui ^{[1
]}

Liu, Jingang ^{[1
,2
]}

机构：

[1] Capital Normal Univ, Beijing 100048, Peoples R China

[2] Chinese Acad Sci, Inst Comp Technol, Beijing 100089, Peoples R China

来源：

PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON ELECTRONIC, MECHANICAL, INFORMATION AND MANAGEMENT SOCIETY (EMIM) | 2016年 / 40卷

关键词：

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

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：395 / 401

页数：7

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