DocumentCode
3740877
Title
Visualization of similar news articles with network analysis and text mining
Author
Takayuki Imai;Keita Nakamura;Toshiaki Ohmameuda
Author_Institution
Advanced Production Systems Engineering Course, National Institute of Technology, Gunma College, Japan
fYear
2015
Firstpage
151
Lastpage
152
Abstract
This paper proposes the method to classify news articles by combining tf-idf and n-gram. This method extracts characteristic words from each news article and classify news based on these words. Numerical experiment results show the relationship among the news articles and visualize the similar articles with networks. Additionally, the authors compare proposal method with only tf-idf in order to verify the effectiveness of this method.
Keywords
"Visualization","Electronic mail","Text mining","Computers","Uniform resource locators","Natural language processing","Mathematical model"
Publisher
ieee
Conference_Titel
Consumer Electronics (GCCE), 2015 IEEE 4th Global Conference on
Type
conf
DOI
10.1109/GCCE.2015.7398571
Filename
7398571
Link To Document