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Social-based traffic information extraction and classification

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4 Author(s)
Wanichayapong, N. ; Nat. Electron. & Comput. Technol. Center (NECTEC), Nat. Sci. & Technol. Dev. Agency (NSTDA), Pathumthani, Thailand ; Pruthipunyaskul, W. ; Pattara-atikom, W. ; Chaovalit, P.

Social networks such as Twitter and Facebook are popular, personal, and real-time in nature. We found that there exists a significant number of traffic information such as traffic congestion, incidents, and weather in Twitter. However, an algorithm is needed to extract and classify the traffic information before publishing (re-tweeting) and becoming useful for others. Traffic information was extracted from Twitter using syntactic analysis and then further classified into two categories: point and link. This method can classify 2,942 traffic tweets into the point category with 76.85% accuracy and classify 331 traffic tweets into the link category with 93.23% accuracy. Our system can report traffic information real-time.

Published in:

ITS Telecommunications (ITST), 2011 11th International Conference on

Date of Conference:

23-25 Aug. 2011

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