Download Hotspot Detection and Analysis in Chat Environment

Asian Research Consortium
Asian Journal of Research in Social Sciences and Humanities
Vol. 6, No. 7, July 2016, pp. 1000-1007
Asian Journal
of Research in
Social Sciences
and
Humanities
ISSN 2249-7315
A Journal Indexed in Indian Citation Index
www.aijsh.com
DOI NUMBER: 10.5958/2249-7315.2016.00483.4
Category:Science and Technology
Hotspot Detection and Analysis in Chat Environment
Dr. K. Nirmala Devi*
*Kongu Engineering College,
Perundurai.
Abstract
In the recent age of internet, one of the media that is quite commonly used by many people is social
media which has become an important information resource for showing the happenings around the
globe. A fast development in the social media like discussion forums, e-mail and chat environment
resources are contributing greatly to the collective knowledge that remains unused. The instant
communication is effectively attracting by many people in the chat environment. The sparseness of
those data is a challenging for analysis. Therefore, it is necessary to understand the opinion of the
users by analyzing the conversations. Determination of chat conversation topic is one of the
important areas in chat mining. The conversation of the chat data is stored in the log files and that
plays major role for performing the analysis. The proposed system aims to identify which topic or
incident is more predominant in the world of cricket for a particular period of time. In order to
identify the possible topics of the chat, the cricket news and posts collected from cricket sites and
are grouped by clustering. The matching between chat log data and cricket site data is performed,
where centroid of the cluster helps to decide the hot topic in the corresponding time window. The
experiment on real data set provides meaningful information and significantly very much useful.
Keywords: Hotspot, Text mining, K-means, Enhanced K-means, Social Media.
References
Earle, PS, Bowden, DC & Guy, M, “Twitter Earthquake Detection: Earthquake Monitoring in a
Social World”, Annals Geophysics,Volume 54, No. 6, 2011.
Mark Dredze , “How Social Media Will Change Public Health”, IEEE Intelligent Systems, vol.27,
Volume 04, Pages 81-84, 2012.
Ozcan, Ozyurt and Cemal Kose, “Chat mining: Automatically determination of chat conversations
topic in Turkish text based chat mediums”, Expert System with Applications, Volume 37,
Pages 8705-8710, 2010.
Tayfun Kucukyilmaz , B. Barla Cambazoglu, Cevdet Aykanat, Fazli, “Can Chat mining :
Predicting user and message attributes in computer-mediated communication”,
Volume 44, Pages 1448-1466, 2008.
1000
Devi (2016). Asian Journal of Research in Social Sciences and Humanities,
Vol. 6, No.7, pp. 1000-1007.
Haichao, D., Siu, C. H., & Yulan, H., “Structural analysis of chat messages for topic
detection.Online Information Review”, Volume 30, No 5, Pages 496–516, 2006.
Hong Liu & Xiaojun Li, “Internet Public Opinion Hotspot Detection Research Based on K-means
Algorithm”, ICSI 2010, Part II, LNCS 6146, pp. 594–602, Springer-Verlag Berlin
Heidelberg, 2010.
Herring and Paolillo, J. C., “Gender and genre variations in Weblogs”, Journal of Sociolinguistics,
Volume 10, No4, Pages 439–459, 2006.
Herring and Danet, “Multilingual Internet: Language, culture, and communication online”, New
York: Oxford University Press, 2007.
https://frug.github.io/AJAX-Chat/
http://chat.kongu.edu
http://cricket.yahoo.com/news/
http://www.espncricinfo.com/ci/content/story/news.html
Andrew, B & Lawson, “Hotspot detection and clustering: ways and means”, Environ Ecol Stat,
2010, Volume 17, Pages 231–245, 2010.
Gruhl, D, Guha, R, Kumar, R, Novak, J & Tomkins, A, “The predictive power of online chatter”,
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge
discovery in data mining, Pages 78-87, 2005.
1001