Abstract
During the last decade social media have generated tons of data, that is the primal information resource for multiple applications. Analyzing this information let us to discover almost immediately unusual situations, such as traffic jumps, traffic accidents, state of the roads, etc.. This research proposes an approach for classifying pollution and traffic tweets automatically. Taking advantage of the information in tweets, it evaluates several machine learning supervised algorithms for text classification, where it determines that the support vector machine (SVM) algorithm achieves the highest accuracy value of 85,8% classifying events of traffic and not traffic. Furthermore, to determine the events that correspond to traffic or pollution we perform a multiclass classification. Where we obtain an accuracy of 78.9%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
D’Andrea, E., Ducange, P., Lazzerini, B., Marcelloni, F.: Real-time detection of traffic from twitter stream analysis. IEEE Trans. Intell. Transp. Syst. 16(4), 2269–2283 (2015)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Elsevier, Amsterdam (2014)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
Aiello, L.M., et al.: Sensing trending topics in Twitter. IEEE Trans. Multimed. 15(6), 1268–1282 (2013)
Patil, L.H., Atique, M.: A novel feature selection based on information gain using WordNet. In: Proceedings of SAI Conference, London, UK, pp. 625–629 (2013)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6(1), 37–66 (1991)
Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ijcai, Montreal, Canada, vol. 14, pp. 1137–1145 (1995)
Zeng, Z.-Q., Yu, H.-B., Xu, H.-R., Xie, Y.-Q., Gao, J.: Fast training support vector machines using parallel sequential minimal optimization. In: 3rd International Conference on Intelligent System and Knowledge Engineering, ISKE 2008, vol. 1, pp. 997–1001. IEEE (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chamorro, V. et al. (2020). Twitter Mining for Multiclass Classification Events of Traffic and Pollution. In: Ahram, T., Karwowski, W., Pickl, S., Taiar, R. (eds) Human Systems Engineering and Design II. IHSED 2019. Advances in Intelligent Systems and Computing, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-27928-8_153
Download citation
DOI: https://doi.org/10.1007/978-3-030-27928-8_153
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27927-1
Online ISBN: 978-3-030-27928-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)