Empirical Study of Online News Classification Using Machine Learning Approaches

被引：0
|
作者：
Suleymanov, Umid [1 ]
Rustamov, Samir [2 ]
Zulfugarov, Murad [2 ]
Orujov, Orkhan [3 ]
Musayev, Nadir [2 ]
Alizade, Azar [2 ]
机构：
[1] ADA Univ, ASAN Serv, Sch Informat Technol & Engn, Baku, Azerbaijan
[2] ADA Univ, Sch Informat Technol & Engn, Baku, Azerbaijan
[3] Azerbaijan Univ Architecture & Construct, Informat Syst, Baku, Azerbaijan
来源：
2018 IEEE 12TH INTERNATIONAL CONFERENCE ON APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES (AICT) | 2018年
关键词：
text classification; news classification; Naive Bayes; Support Vector Machines; Artificial Neural Networks; Stemming; Feature Reduction;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The developed text classification system is designed to automate news classification process. Text corpus for training and testing the system is formed from Azerbaijani news articles. The system will be useful for online news categorization and automatic news labeling for news agencies. Naive Bayes, Support Vector Machines, and Artificial Neural Networks have been implemented to solve the news classification problem. Moreover, a number of approaches such as stemming, stop word removal, feature reduction have been implemented for both performance and accuracy improvements.
引用
收藏
页码：152 / 157
页数：6
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