Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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