This paper is published in Volume-2, Issue-6, 2016
Area
Data Mining (News Classification)
Author
Kamaldeep kaur, Maninder Kaur
Org/Univ
Doaba Institute of Engineering & Technology, Kharar, India
Pub. Date
07 December, 2016
Paper ID
V2I6-1179
Publisher
Keywords
News Classification, Regression, Probabilistic Classifier, Automatic Categorization, Multi-domain news analysis

Citationsacebook

IEEE
Kamaldeep kaur, Maninder Kaur. k-Means Clustering based Lexicon Analytical Model for Multi-Source News Classification, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Kamaldeep kaur, Maninder Kaur (2016). k-Means Clustering based Lexicon Analytical Model for Multi-Source News Classification. International Journal of Advance Research, Ideas and Innovations in Technology, 2(6) www.IJARIIT.com.

MLA
Kamaldeep kaur, Maninder Kaur. "k-Means Clustering based Lexicon Analytical Model for Multi-Source News Classification." International Journal of Advance Research, Ideas and Innovations in Technology 2.6 (2016). www.IJARIIT.com.

Abstract

The supervised models have been found more efficient for the purpose of news classification. The major goal of the news classification research is to improve the accuracy while decreasing the elapsed time. It is always difficult for the people to read all of the news on their favourites portal which have listed over the given portal. In this research, an approach is KNN lexicon technique which is used to obtain the popular news list from thousands or hundreds of online news available through APIs. This approach uses extraction summarization for summarizing the keywords thereby selecting the original sentences and putting it together into a new shorter text explaining the overall overview of the news data. Then the lexicon analysis would be performed over the given text data and then final classification of the news is done using k-nearest neighbor. The results would be obtained in the form of the parameters of accuracy, elapsed time, etc.