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DOI: 10.14569/IJACSA.2019.0100343
PDF

ATAM: Arabic Traffic Analysis Model for Twitter

Author 1: Amani AlFarasani
Author 2: Tahani AlHarthi
Author 3: Sarah AlHumoud

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 3, 2019.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Harvesting Twitter for insight and meaning in what is called sentiment analysis (SA) is a major trend stemming from computational linguistics and AI. Industry and academia are interested in maximizing efficiency while mining text to attain the most currently available data and crowdsourcing opinions. In this study, we present the ATAM model for traffic analysis using the data available on Twitter. The model comprises five components that start with data streaming and collection and ends with the road incident prediction through classification. The classification of data is done using a lexicon-based method. The predicted classes are as follows: safe, needs attention, dangerous, and neutral. The data were collected for three months in the city of Riyadh, Saudi Arabia. The model was applied on 10k tweets with an overall accuracy of the model classifying all four classes of 82%.

Keywords: Data mining; machine learning; sentiment analysis; unsupervised learning; lexicon-based; support vector machines

Amani AlFarasani, Tahani AlHarthi and Sarah AlHumoud, “ATAM: Arabic Traffic Analysis Model for Twitter” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100343

@article{AlFarasani2019,
title = {ATAM: Arabic Traffic Analysis Model for Twitter},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100343},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100343},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Amani AlFarasani and Tahani AlHarthi and Sarah AlHumoud}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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