Crime alert! crime typification in news based on text mining

H. Alatrista-Salas, J. Morzán-Samamé, M. Nunez-del-Prado

Producción científica: Capítulo del libro/informe/acta de congresoCapítulo de libro

2 Citas (Scopus)

Resumen

In this paper we detailed a multinomial classification-based methodology that combines different algorithms (SVM and MLP) with document representations (Tf Idf vectorization and Doc2vec embedding) and: (i) can distinguish between crime-related news and not-crime related news and; (ii) allows the assignment of each crime-related news to its corresponding crime type. With a F1-score of 84% achieved by the MLP with Doc2vec approach, it can be concluded that it is possible to answer the question of how the crimes are committed (what types of crime are perpetrated) and, in this way, offer a thermometer to citizens about criminal activity in a given territory, as reported by news articles.

Idioma originalInglés
Título de la publicación alojadaLecture Notes in Networks and Systems
Subtítulo de la publicación alojadaProceedings of the 2019 Future of Information and Communication Conference
EditoresKohei Arai, Rahul Bhatia
Lugar de publicaciónCham
EditorialSpringer Verlag
Páginas725-741
Número de páginas17
ISBN (versión digital)9783030123888
ISBN (versión impresa)9783030123871
DOI
EstadoPublicada - 2020

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen69

Nota bibliográfica

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

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