This paper is published in Volume-2, Issue-5, 2016
Area
Data Mining
Author
Rucha Kulkarni, Sayali Dhanawade, Shraddha Raut, Prof.D.S.Lavhkarer
Org/Univ
NESGOI,Pune, India
Keywords
Traffic event detection, tweet classification, social sensing, text mining.
Citations
IEEE
Rucha Kulkarni, Sayali Dhanawade, Shraddha Raut, Prof.D.S.Lavhkarer. Twitter Stream Analysis for Traffic Detection in Real Time, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Rucha Kulkarni, Sayali Dhanawade, Shraddha Raut, Prof.D.S.Lavhkarer (2016). Twitter Stream Analysis for Traffic Detection in Real Time. International Journal of Advance Research, Ideas and Innovations in Technology, 2(5) www.IJARIIT.com.
MLA
Rucha Kulkarni, Sayali Dhanawade, Shraddha Raut, Prof.D.S.Lavhkarer. "Twitter Stream Analysis for Traffic Detection in Real Time." International Journal of Advance Research, Ideas and Innovations in Technology 2.5 (2016). www.IJARIIT.com.
Rucha Kulkarni, Sayali Dhanawade, Shraddha Raut, Prof.D.S.Lavhkarer. Twitter Stream Analysis for Traffic Detection in Real Time, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Rucha Kulkarni, Sayali Dhanawade, Shraddha Raut, Prof.D.S.Lavhkarer (2016). Twitter Stream Analysis for Traffic Detection in Real Time. International Journal of Advance Research, Ideas and Innovations in Technology, 2(5) www.IJARIIT.com.
MLA
Rucha Kulkarni, Sayali Dhanawade, Shraddha Raut, Prof.D.S.Lavhkarer. "Twitter Stream Analysis for Traffic Detection in Real Time." International Journal of Advance Research, Ideas and Innovations in Technology 2.5 (2016). www.IJARIIT.com.
Abstract
Now a days,social networking are more popular.for example,twitter,Facebook etc.social networking are used forevent detection in real time.Real time events are traffic detection,earthquake monitoring.In this paper,we use the the twitter for real time traffic event detection.Firstly,the system extract the tweets from twitter and apply the text mining techniques on that tweets.those techniques are tokenization, stop-word removing,stemming.after that classify that on the basis of class label i.e traffic event or no traffic event.In this paper, we present an online method for detection of real-traffic events in Twitter data.