Comparative Analysis of Feature Extraction Techniques for Event Detection from News Channels' Facebook Page

Authors

  • Farzana Kabir Ahmad School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia

Keywords:

Text Mining, Event Detection, Feature Extraction Technique, News Channels, Social Network Sites,

Abstract

Event detection from the Social Network sites (SNs) has attracted significant attention of many researchers to understand user perceptions and opinions on certain incidents that have occurred. Facebook is the most famous SNs among internet users to express their opinions, emotions and thoughts. Due to its popularity, many news channels such as BBC have created a Facebook page to allow reader to comment on news reported, which has led to an explosion of user-generated data posted on the Internet. Monitoring and analyzing this rich and continuous user-generated content can yield unprecedentedly valuable information, enabling users and organizations to acquire actionable knowledge. Previously, in the context of text mining research, various feature extraction techniques have been proposed to extract relevant key features that could be used to detect the news posts into corresponding event. However, these techniques are separately tested on different data. Moreover, analyzing large number of news posts over a period of time is a challenging task due to its complex properties and unstructured data. Thus, this paper has proposed a comparative analysis on various types of feature extraction techniques on three different classifiers, namely Support Vector Machine (SVM), Naïve Bayes (NB) and KNearest Neighbor (kNN). The aim of this research is to discover the appropriate feature extraction technique and classifier that could correct detect event and offer optimal accuracy result. This analysis has been tested on three news channels datasets, namely, BBC, Aljazeera, and Al-Arabiya news channels. The experimental results have shown that Chisquare and SVM has proven to be a better extraction and classifier technique compared to other techniques with optimal accuracy of 92.29%, 87.12%, 87.00% have been observed in BBC, Aljazeera, and Al-Arabiya news channels respectively

Downloads

Published

2017-03-01

How to Cite

Kabir Ahmad, F. (2017). Comparative Analysis of Feature Extraction Techniques for Event Detection from News Channels’ Facebook Page. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-2), 13–17. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1643

Issue

Vol. 9 No. 1-2: Advancing Digital Societies for Social and Cultural Improvement

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.