A Model for Clustering Social Media Data for Electronic Learning

Erick Odhiambo Omuya

doi:doi:10.11648/j.ajai.20170101.11

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A Model for Clustering Social Media Data for Electronic Learning

Erick Odhiambo Omuya

Published in American Journal of Artificial Intelligence (Volume 1, Issue 1)

Received: 21 April 2017 Accepted: 11 May 2017 Published: 3 July 2017

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Abstract

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.

Published in	American Journal of Artificial Intelligence (Volume 1, Issue 1)
DOI	10.11648/j.ajai.20170101.11
Page(s)	1-4
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Social Media, Twitter Application Programming Interface, Natural Language Processing, Twitter, Corpus

References

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[2]	Yardi, S.; Romero, D.; Schoenebeck, G.; and Boyd, D. (2010). Detecting spam in a twitter network. First Monday 15: 1–4.
[3]	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.
[4]	Brook, C. and Oliver, R. (2003). Online learning communities: Investigating a design framework. Australian Journal of Educational Technology, 19 (2), 139-160.
[5]	Nichols, M. (2003). A theory of eLearning. Educational Technology & Society, 6 (2), 1−10.
[6]	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.
[7]	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.
[8]	Griffiths, T. L., Steyvers, M., &Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114, 211-244. (pdf)(topic modeling toolbox) .
[9]	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.
[10]	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.
[11]	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.

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APA Style

Erick Odhiambo Omuya. (2017). A Model for Clustering Social Media Data for Electronic Learning. American Journal of Artificial Intelligence, 1(1), 1-4. https://doi.org/10.11648/j.ajai.20170101.11

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Erick Odhiambo Omuya. A Model for Clustering Social Media Data for Electronic Learning. Am. J. Artif. Intell. 2017, 1(1), 1-4. doi: 10.11648/j.ajai.20170101.11

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Erick Odhiambo Omuya. A Model for Clustering Social Media Data for Electronic Learning. Am J Artif Intell. 2017;1(1):1-4. doi: 10.11648/j.ajai.20170101.11

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@article{10.11648/j.ajai.20170101.11,
  author = {Erick Odhiambo Omuya},
  title = {A Model for Clustering Social Media Data for Electronic Learning},
  journal = {American Journal of Artificial Intelligence},
  volume = {1},
  number = {1},
  pages = {1-4},
  doi = {10.11648/j.ajai.20170101.11},
  url = {https://doi.org/10.11648/j.ajai.20170101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20170101.11},
  abstract = {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.},
 year = {2017}
}

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TY  - JOUR
T1  - A Model for Clustering Social Media Data for Electronic Learning
AU  - Erick Odhiambo Omuya
Y1  - 2017/07/03
PY  - 2017
N1  - https://doi.org/10.11648/j.ajai.20170101.11
DO  - 10.11648/j.ajai.20170101.11
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 1
EP  - 4
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20170101.11
AB  - 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.
VL  - 1
IS  - 1
ER  -

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Author Information

Erick Odhiambo Omuya

Department of Computing and IT, Zetech University, Nairobi, Kenya

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APA Style

Erick Odhiambo Omuya. (2017). A Model for Clustering Social Media Data for Electronic Learning. American Journal of Artificial Intelligence, 1(1), 1-4. https://doi.org/10.11648/j.ajai.20170101.11

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ACS Style

Erick Odhiambo Omuya. A Model for Clustering Social Media Data for Electronic Learning. Am. J. Artif. Intell. 2017, 1(1), 1-4. doi: 10.11648/j.ajai.20170101.11

Copy | Download

AMA Style

Erick Odhiambo Omuya. A Model for Clustering Social Media Data for Electronic Learning. Am J Artif Intell. 2017;1(1):1-4. doi: 10.11648/j.ajai.20170101.11

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@article{10.11648/j.ajai.20170101.11,
  author = {Erick Odhiambo Omuya},
  title = {A Model for Clustering Social Media Data for Electronic Learning},
  journal = {American Journal of Artificial Intelligence},
  volume = {1},
  number = {1},
  pages = {1-4},
  doi = {10.11648/j.ajai.20170101.11},
  url = {https://doi.org/10.11648/j.ajai.20170101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20170101.11},
  abstract = {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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - A Model for Clustering Social Media Data for Electronic Learning
AU  - Erick Odhiambo Omuya
Y1  - 2017/07/03
PY  - 2017
N1  - https://doi.org/10.11648/j.ajai.20170101.11
DO  - 10.11648/j.ajai.20170101.11
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 1
EP  - 4
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20170101.11
AB  - 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.
VL  - 1
IS  - 1
ER  -

Copy | Download