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基于Word2Vec的微博文本分类研究
引用本文:牛雪莹,赵恩莹.基于Word2Vec的微博文本分类研究[J].计算机系统应用,2019,28(8):256-261.
作者姓名:牛雪莹  赵恩莹
作者单位:太原科技大学 计算机科学与技术学院, 太原 030024,太原科技大学 计算机科学与技术学院, 太原 030024
摘    要:以微博为代表的社交平台是信息时代人们必不可少的交流工具.挖掘微博文本数据中的信息对自动问答、舆情分析等应用研究都具有重要意义.短文本数据的分类研究是短文本数据挖掘的基础.基于神经网络的Word2vec模型能很好的解决传统的文本分类方法无法解决的高维稀疏和语义鸿沟的问题.本文首先基于Word2vec模型得到词向量,然后将类别因素引入传统权重计算方法TF-IDF (Term Frequency-Inverse Document Frequency)设计词向量权重,进而用加权求和的方法得到短文本向量,最后用SVM分类器对短文本做分类训练并且通过微博数据实验验证了该方法的有效性.

关 键 词:Word2Vec  短文本分类  TF-IDF
收稿时间:2019/2/17 0:00:00
修稿时间:2019/3/8 0:00:00

Research on Chinese Weibo Text Classification Based on Word2Vec
NIU Xue-Ying and ZHAO En-Ying.Research on Chinese Weibo Text Classification Based on Word2Vec[J].Computer Systems& Applications,2019,28(8):256-261.
Authors:NIU Xue-Ying and ZHAO En-Ying
Affiliation:School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China and School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
Abstract:The Chinese Weibo is an indispensable communication tool for people today. Mining information in Weibo text is of great significance to automatic question and answer, public opinion analysis and other applied research. The short text classification study is the basis of short text mining. The neural network-based Word2Vec can solve problems of high-dimensional sparseness and semantic gap that traditional text categorization methods cannot solve. This study obtains the word vector based on Word2Vec, then the class factor is introduced into the traditional weight calculation method TF-IDF (Term Frequency-Inverse Document Frequency) to design the word vector weight. Finally, the SVM classifier is used for classification. The effectiveness of the method is verified by experiments on Weibo data.
Keywords:Word2Vec  short text classification  TF-IDF
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