Twitter Stream Analysis for traffic detection in real time

Authors

  • Sayali Dhanawade Department of Computer Engineering, NESGOI, Pune
  • Rucha Kulkarni Department of Computer Engineering, NESGOI, Pune
  • Shraddha Raut Department of Computer Engineering, NESGOI, Pune
  • Prof.D.S.Gogawale Department of Computer Engineering, NESGOI, Pune

Keywords:

Traffic event detection, tweet classification, social sensing, text mining, NLP.

Abstract

Social networking sites have spread in recent year and becoming real time information channel. In this
paper we use the Twitter as social networking site for collecting the real time information. In this paper, we use some
machine learning technique and text mining algorithms for traffic detection. Firstly, we fetch the tweets then apply the
sentiment analysis techniques on that fetched tweets. We use the NLP for extracting the meaningful information. NLP
include tokenization, stop word removing stemming. These NLP method apply on that tweets and after that the classify
tweets into two types i.e. Traffic or Non Traffic event. If there is traffic then show the alternate path to user.

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Published

2017-04-25

How to Cite

Sayali Dhanawade, Rucha Kulkarni, Shraddha Raut, & Prof.D.S.Gogawale. (2017). Twitter Stream Analysis for traffic detection in real time. International Journal of Advance Engineering and Research Development (IJAERD), 4(4), 168–172. Retrieved from https://ijaerd.com/index.php/IJAERD/article/view/4813

Issue

Vol. 4 No. 4 (2017): Volume 4 Issue 4 April 2017

Section

Articles