A Model for Clustering Social Media Data for Electronic Learning
American Journal of Artificial Intelligence
Volume 1, Issue 1, December 2017, Pages: 1-4
Received: Apr. 21, 2017; Accepted: May 11, 2017; Published: Jul. 3, 2017
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Erick Odhiambo Omuya, Department of Computing and IT, Zetech University, Nairobi, Kenya
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Through Social media, people are able to write short messages on their walls to express their sentiments using various social media like Twitter and Facebook. Through these messages also called status updates, they share and discuss things like news, jokes, business issues and what they go through on a daily basis. Tweets and other updates have become so important in the world of information and communication because they have a great potential of passing information very fast. They enable interaction among vast groups of people including students, businesses and their clients. These numerous amounts of information can be extracted, processed and properly utilized in areas like marketing and electronic learning. This paper reports on the successful development of a way of searching, filtering, organizing and storing the information from social media so that it can be put to some good use in an electronic learning environment. This helps in solving the problem of losing vital information that is generated from the social media. It addresses this limitation by using the data from twitter to cluster students and by so doing support group electronic learning.
Social Media, Twitter Application Programming Interface, Natural Language Processing, Twitter, Corpus
To cite this article
Erick Odhiambo Omuya, A Model for Clustering Social Media Data for Electronic Learning, American Journal of Artificial Intelligence. Vol. 1, No. 1, 2017, pp. 1-4. doi: 10.11648/j.ajai.20170101.11
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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