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
Views 1620 Downloads 94
Erick Odhiambo Omuya, Department of Computing and IT, Zetech University, Nairobi, Kenya
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.
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.
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.
Dongwoo, K., Yohan, J., Il-Chul, M., and Oh, A. (2010). Analysis of twitter lists as a potential source for discovering latent characteristics of users. Workshop on Microblogging at the ACM Conference on Human Factors in Computer Systems.
Yardi, S.; Romero, D.; Schoenebeck, G.; and Boyd, D. (2010). Detecting spam in a twitter network. First Monday 15: 1–4.
Light, V, Nesbitt, E, Light, P & Burns, JR. (2000). Let's You and Me Have a Little Discussion: computer mediated communication in support of campus-based university courses, Studies in Higher Education, vol. 25, no. 1.
Brook, C. and Oliver, R. (2003). Online learning communities: Investigating a design framework. Australian Journal of Educational Technology, 19 (2), 139-160.
Nichols, M. (2003). A theory of eLearning. Educational Technology & Society, 6 (2), 1−10.
Benson, A. (2002). Using online learning to meet workforce demand: A case study of stakeholder influence. Quarterly Review of Distance Education, 3 (4), 443−452.
Hiltz, S. R., &Turoff, M. (2005). Education goes digital: The evolution of online learning and the revolution in higher education. Communications of the ACM, 48 (10), 59−64, doi: 10.1145/1089107.1089139.
Griffiths, T. L., Steyvers, M., &Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114, 211-244. (pdf)(topic modeling toolbox) .
Pak, A., &Paroubek, P. (2010). Twitter based system: Using Twitter for disambiguating sentiment ambiguous adjectives. In Proceedings of the 5th International Workshop on Semantic Evaluation (pp. 436-439). Association for Computational Linguistics.
Yessenov, K. and Misailovic, S. (2009). Sentiment Analysis of Movie Review Comments. Available: http://people.csail.mit.edu/kuat/courses/6.863/report.pdf. Last accessed 13th July 2011.
Lu H, Sun S, Lu Y (2006). Preprocessing data for effective classification. ACM SIGMOD’96 workshop on research issues on data mining and knowledge discovery, Montreal, QC Nichols, M. (2006). A theory of eLearning. Educational Technology & Society, 6 (2), 1−10.