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), 2017. Published by Science Publishing Group |
Social Media, Twitter Application Programming Interface, Natural Language Processing, Twitter, Corpus
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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
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
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Download@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}
}
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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