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Classification of Traffic Events Notified in Social Networks' Texts

Classification of Traffic Events Notified in Social Networks' Texts

Ana Maria Magdalena Saldana-Perez, Marco Antonio Moreno-Ibarra, Miguel Jesus Torres-Ruiz
Copyright: © 2018 |Pages: 12
ISBN13: 9781522522553|ISBN10: 1522522557|EISBN13: 9781522522560
DOI: 10.4018/978-1-5225-2255-3.ch604
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MLA

Saldana-Perez, Ana Maria Magdalena, et al. "Classification of Traffic Events Notified in Social Networks' Texts." Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2018, pp. 6973-6984. https://doi.org/10.4018/978-1-5225-2255-3.ch604

APA

Saldana-Perez, A. M., Moreno-Ibarra, M. A., & Torres-Ruiz, M. J. (2018). Classification of Traffic Events Notified in Social Networks' Texts. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Fourth Edition (pp. 6973-6984). IGI Global. https://doi.org/10.4018/978-1-5225-2255-3.ch604

Chicago

Saldana-Perez, Ana Maria Magdalena, Marco Antonio Moreno-Ibarra, and Miguel Jesus Torres-Ruiz. "Classification of Traffic Events Notified in Social Networks' Texts." In Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., 6973-6984. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2255-3.ch604

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Abstract

It is interesting to exploit the user generated content (UGC), and to use it with a view to infer new data; volunteered geographic information (VGI) is a concept derived from UGC, which main importance lies in its continuously updated data. The present approach tries to explode the use of VGI, by collecting data from a social network and a RSS service; the short texts collected from the social network are written in Spanish language; a text mining and a recovery information processes are applied over the data, in order to remove special characters on text, and to extract relevant information about the traffic events on the study area, then data are geocoded. The texts are classified by using a machine learning algorithm into five classes, each of them represents a specific traffic event or situation.

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