Twitter Mining for Multiclass Classification Events of Traffic and Pollution

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.

• Autor Principal: Chamorro, Verónica (author)
• Outros autores: Rivera, Richard (author), Varela-Aldás, José (author), Castillo-Salazar, David (author), Borja-Galeas, Carlos (author), Guevara-Maldonado, César (author), Arias-Flores, Hugo (author), Fierro-Saltos, Washington (author), Hidalgo-Guijarro, Jairo (author), Yandún-Velasteguí, Marco (author)
• Formato: article
• Idioma: eng
• Publicado: 2020
• Acceso en liña: https://link.springer.com/chapter/10.1007/978-3-030-27928-8_153; https://hdl.handle.net/20.500.14809/3422