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Twitter Mining for Multiclass Classification Events of Traffic and Pollution

  • Verónica Chamorro
  • Richard RiveraEmail author
  • José Varela-Aldás
  • David Castillo-Salazar
  • Carlos Borja-Galeas
  • Cesar Guevara
  • Hugo Arias-Flores
  • Washington Fierro-Saltos
  • Jairo Hidalgo-Guijarro
  • Marco Yandún-Velasteguí
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1026)

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%.

Keywords

Twitter event detection Pollution detection Traffic detection Twitter mining Algorithms of classification SVM 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Verónica Chamorro
    • 1
  • Richard Rivera
    • 2
    Email author
  • José Varela-Aldás
    • 3
  • David Castillo-Salazar
    • 3
    • 6
  • Carlos Borja-Galeas
    • 4
    • 9
  • Cesar Guevara
    • 5
  • Hugo Arias-Flores
    • 5
  • Washington Fierro-Saltos
    • 6
    • 7
  • Jairo Hidalgo-Guijarro
    • 8
  • Marco Yandún-Velasteguí
    • 8
  1. 1.Facultad de InformáticaUniversidad Complutense de MadridMadridSpain
  2. 2.Escuela de Formación de TecnólogosEscuela Politécnica NacionalQuitoEcuador
  3. 3.SISAu Research GroupUniversidad IndoaméricaAmbatoEcuador
  4. 4.Facultad de Arquitectura, Artes y DiseñoUniversidad IndoaméricaQuitoEcuador
  5. 5.Mechatronics and Interactive Systems - MIST Research CenterUniversidad IndoaméricaQuitoEcuador
  6. 6.Facultad de InformáticaUniversidad Nacional de la PlataBuenos AiresArgentina
  7. 7.Facultad de Ciencias de la EducaciónUniversidad Estatal de BolívarGuanujoEcuador
  8. 8.Grupo de Investigación GISATUniversidad Politécnica Estatal del CarchiTulcanEcuador
  9. 9.Facultad de Diseño y ComunicaciónUniversidad de PalermoBuenos AiresArgentina

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