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
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"
Conference_Titel :
Consumer Electronics (GCCE), 2015 IEEE 4th Global Conference on
DOI :
10.1109/GCCE.2015.7398571
