A comparison of textual data mining methods for sex identification in chat conversations

被引：0

作者：

Kose, Cemal ^{[1
]}

Ozyurt, Ozcan ^{[1
]}

Ikibas, Cevat ^{[1
]}

机构：

[1] Karadeniz Tech Univ, Fac Engn, Dept Comp Engn, TR-61080 Trabzon, Turkey

来源：

INFORMATION RETRIEVAL TECHNOLOGY | 2008年 / 4993卷

关键词：

mining chat conversations; sex identification; information extraction; text mining; machine learning;

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：

Mining textual data in chat mediums is becoming more important because these mediums contain a vast amount of information, which is potentially relevant to a society's current interests, habits, social behaviors, crime tendency and other tendencies. Here, sex identification is taken as a base study in information mining in chat mediums. In order to do this, a simple discrimination function and semantic analysis method are proposed for sex identification in Turkish chat mediums. Then, the proposed sex identification method is compared with the Support Vector Machine (SVM) and Naive Bayes (NB) methods. Finally, results show that the proposed system has achieved accuracy over 90% in sex identification.

引用

页码：638 / 643

页数：6

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