Real-Time Classification of Twitter Trends using Support Vector Machine with Location Tracking
Vijayan Ramaraj1, G Gunasekaran2, Prasanna Santhanam3, S Meenatchi4, Kishore Kumar K5
1Kishore Kumar K, Department of Information Technology and Engineering, SITE, Vellore Institute of Technology Katpadi, Vellore, (Tamil Nadu), India.
2Prasanna Santhanam, Associate Professor, Department of Information Technology and Engineering, Vellore Institute of Technology, Katpadi, Vellore, (Tamil Nadu), India.
3S.Meenatchi Assistant Professor, Department of Information Technology and Engineering, Vellore Institute of Technology, Katpadi, Vellore, (Tamil Nadu), India.
4G.Gunasekaran, Assistant Professor, Department of Information Technology and Engineering, Katpadi, Vellore, (Tamil Nadu), India.
5Vijayan Ramaraj, Associate Professor, Department of Information Technology and Engineering, Vellore Institute of Technology, Katpadi, Vellore, (Tamil Nadu), India. India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 359-367 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3512048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In Social Network World Wide there are a Lot of people having twitter accounts. Every new and old user trying to create a new account in twitter for his own purposes like they can follow and tweet to the celebrity and the popular persons but some users giving the fake details in their profile like country, place, and locations. It may happen for fake data information and tweets, followers and unwanted messages can be posted on Twitter. Here using the geographical tracking location system with the help of longitude and latitude of geo-location mapping system. So users’ can register an account with the current location only, if anyone trying to create the duplicate location that user information will be blocked and the details of latitude and longitude information can be sent to the admin with the current location of duplicate users’. It proposes to validate its effectiveness through the experiments.
Keyword: Geo-Location, Twitter, Real-Time, Classification
Scope of the Article: Classification