计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (23): 159-161.DOI: 10.3778/j.issn.1002-8331.2010.23.045
• 数据库、信号与信息处理 • 上一篇 下一篇
刘金岭
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LIU Jin-ling
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摘要： 为了对中文短信文本进行快速的舆情预测，利用对同义关系词汇归并和上下位词汇聚焦以及种子词汇的确定来实现对短信文本空间的降维，而后又给出了海量短信文本分类的算法及分类主题的提取。实验表明该方法可以大大提高舆情预测的速度和质量。
关键词: 分类, 短信文本, 降维, 主题
Abstract: In order to predict Chinese short message opinion quickly，the short message text dimensionality reduction is obtained by using synonymous terms and the upper and lower merge，and then the mass short message classification algorithm for the extraction and classification of the subject is given.Test shows that the method can greatly improve the public opinion of the speed and quality of forecasts.
Key words: classification, short message text, dimensionality reduction, theme
中图分类号:
TP311
刘金岭. 基于降维的短信文本语义分类及主题提取[J]. 计算机工程与应用, 2010, 46(23): 159-161.
LIU Jin-ling. Dimensionality reduction of short message text classification and thematic extraction of semantic[J]. Computer Engineering and Applications, 2010, 46(23): 159-161.
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链接本文: http://cea.ceaj.org/CN/10.3778/j.issn.1002-8331.2010.23.045
http://cea.ceaj.org/CN/Y2010/V46/I23/159
