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Using Natural Language Processing for Extracting GeoSpatial Urban Issues Complaints from TV News

Authors:
Rich Elton Carvalho Ramalho
Anderson Almeida Firmino
Claudio De Souza Baptista
Ana Gabrielle Ramos Falcão
Maxwell Guimarães de Oliveira
Fabio Gomes de Andrade

Keywords: Geosocial network; NLP; Urban Issues; Crowdsourcing

Abstract:
Citizens as sensors enable the engagement of society through technology to complain on urban issues. Despite the fact that some geosocial networks have been developed in recent years to enable citizens to report many types of urban problems, it is possible to notice that the engagement of the users of these networks usually decreases in time. Hence, many relevant issues are not identified or published, which reduces the effectiveness of these networks. Aiming to overcome this limitation, this paper proposes an approach in which urban issues are automatically detected from a TV news program. The proposed solution uses geoparsing and Natural Language Processing (NLP) techniques to geocode and classify the identified complaints and publishes the results in Crowd4City, a geosocial Network that deals specifically with urban issues. Finally, our method was evaluated using data of a real news TV program in Brazil. Our results indicate 59.8% of success on extracting text and location from the video news.

Pages: 42 to 47

Copyright: Copyright (c) IARIA, 2020

Publication date: March 22, 2020

Published in: conference

ISSN: 2308-393X

ISBN: 978-1-61208-762-7

Location: Valencia, Spain

Dates: from November 21, 2020 to November 25, 2020