Text mining and analytics a case study from news channels posts on Facebook

Nowadays, social media has swiftly altered the media landscape resulting in a competitive environment of news creation and dissemination. Sharing news through social media websites is almost provided in a textual format. The nature of the disseminated text is considered as unstructured text. Text mi...

Full description

Bibliographic Details
Main Authors:	Chaker, Mhamdi, Al-Emran, Mostafa, Salloum, Said A.
Format:	Book Section
Language:	English
Published:	Springer 2017
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/21628/ http://umpir.ump.edu.my/id/eprint/21628/ http://umpir.ump.edu.my/id/eprint/21628/ http://umpir.ump.edu.my/id/eprint/21628/1/5.%20Text%20Mining%20and%20Analytics%20A%20Case%20Study%20from%20News%20Channels%20Posts%20on%20Facebook.pdf

id	ump-21628
recordtype	eprints
spelling	ump-216282019-03-20T04:22:37Z http://umpir.ump.edu.my/id/eprint/21628/ Text mining and analytics a case study from news channels posts on Facebook Chaker, Mhamdi Al-Emran, Mostafa Salloum, Said A. QA76 Computer software Nowadays, social media has swiftly altered the media landscape resulting in a competitive environment of news creation and dissemination. Sharing news through social media websites is almost provided in a textual format. The nature of the disseminated text is considered as unstructured text. Text mining techniques play a significant role in transforming the unstructured text into informative knowledge with various interesting patterns. Due to the lack of literature on textual analysis of news channels’ in social media, the current study seeks to explore this genre of new media discourse through analyzing news channels online textual data and transforming its quantifiable information into constructive knowledge. Accordingly, this study applies various text mining techniques on this under-researched context aiming at extracting knowledge from unstructured textual data. To this end, three news channels have been selected, namely Fox News, CNN, and ABC News. Data has been collected from the Facebook pages of these three news channels through Facepager tool which was then processed using RapidMiner tool. Findings indicated that USA elections news received the highest coverage among others in these channels. Moreover, results revealed that the most frequent shared posts regarding the USA elections were tackled by the CNN followed by ABC News, and Fox News, respectively. Additionally, results revealed a significant relationship between ABC News and CNN in covering similar topics. Springer 2017-11-18 Book Section PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/21628/1/5.%20Text%20Mining%20and%20Analytics%20A%20Case%20Study%20from%20News%20Channels%20Posts%20on%20Facebook.pdf Chaker, Mhamdi and Al-Emran, Mostafa and Salloum, Said A. (2017) Text mining and analytics a case study from news channels posts on Facebook. In: Intelligent Natural Language Processing: Trends and Applications. Springer, Berlin, Germany, pp. 399-415. ISBN 9783319670560 https://doi.org/10.1007/978-3-319-67056-0_19 https://doi.org/10.1007/978-3-319-67056-0_19
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Chaker, Mhamdi Al-Emran, Mostafa Salloum, Said A. Text mining and analytics a case study from news channels posts on Facebook
description	Nowadays, social media has swiftly altered the media landscape resulting in a competitive environment of news creation and dissemination. Sharing news through social media websites is almost provided in a textual format. The nature of the disseminated text is considered as unstructured text. Text mining techniques play a significant role in transforming the unstructured text into informative knowledge with various interesting patterns. Due to the lack of literature on textual analysis of news channels’ in social media, the current study seeks to explore this genre of new media discourse through analyzing news channels online textual data and transforming its quantifiable information into constructive knowledge. Accordingly, this study applies various text mining techniques on this under-researched context aiming at extracting knowledge from unstructured textual data. To this end, three news channels have been selected, namely Fox News, CNN, and ABC News. Data has been collected from the Facebook pages of these three news channels through Facepager tool which was then processed using RapidMiner tool. Findings indicated that USA elections news received the highest coverage among others in these channels. Moreover, results revealed that the most frequent shared posts regarding the USA elections were tackled by the CNN followed by ABC News, and Fox News, respectively. Additionally, results revealed a significant relationship between ABC News and CNN in covering similar topics.
format	Book Section
author	Chaker, Mhamdi Al-Emran, Mostafa Salloum, Said A.
author_facet	Chaker, Mhamdi Al-Emran, Mostafa Salloum, Said A.
author_sort	Chaker, Mhamdi
title	Text mining and analytics a case study from news channels posts on Facebook
title_short	Text mining and analytics a case study from news channels posts on Facebook
title_full	Text mining and analytics a case study from news channels posts on Facebook
title_fullStr	Text mining and analytics a case study from news channels posts on Facebook
title_full_unstemmed	Text mining and analytics a case study from news channels posts on Facebook
title_sort	text mining and analytics a case study from news channels posts on facebook
publisher	Springer
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/21628/ http://umpir.ump.edu.my/id/eprint/21628/ http://umpir.ump.edu.my/id/eprint/21628/ http://umpir.ump.edu.my/id/eprint/21628/1/5.%20Text%20Mining%20and%20Analytics%20A%20Case%20Study%20from%20News%20Channels%20Posts%20on%20Facebook.pdf
first_indexed	2023-09-18T22:31:49Z
last_indexed	2023-09-18T22:31:49Z
_version_	1777416336265183232

Text mining and analytics a case study from news channels posts on Facebook

Similar Items