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Twitter Mining for Multiclass Classification Events of Traffic and Pollution
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Twitter Mining for Multiclass Classification Events of Traffic and Pollution
Journal
Advances in Intelligent Systems and Computing
Date Issued
2020
Author(s)
Chamorro V.
Rivera R.
Varela Aldas, José
Centro de Investigación de Ciencias Humanas y de la Educación
Castillo Salazar, David Ricardo
Facultad de Ciencias de la Educación
Borja Galeas, Carlos
Facultad de Ciencias Económicas, Administrativas y Negocios
Guevara Maldonado, César Byron
Centro de investigación en Mecatrónica y Sistemas Interactivos
Arias Flores, Hugo Patricio
Centro de investigación en Mecatrónica y Sistemas Interactivos
Fierro-Saltos W.
Hidalgo-Guijarro J.
Yandún-Velasteguí M.
Type
Conference Paper
DOI
10.1007/978-3-030-27928-8_153
URL
https://cris.indoamerica.edu.ec/handle/123456789/8934
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%. © Springer Nature Switzerland AG 2020.
Subjects
Attention; Blinking; ...
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6
Acquisition Date
Dec 6, 2024
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