Crime Alert! Crime Typification in News Based on Text Mining

  • Hugo Alatrista-Salas
  • Juandiego Morzán-Samamé
  • Miguel Nunez-del-PradoEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)


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.


Crime analysis Text mining Classification Word vectorization and embeddings 



We would like to thank the iMedia company and its general manager Fernando Gonzalez for the constant effort and communication in the construction and delivery of the database, as well as for all the support provided in the process.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hugo Alatrista-Salas
    • 1
  • Juandiego Morzán-Samamé
    • 1
  • Miguel Nunez-del-Prado
    • 1
    Email author
  1. 1.Universidad del PacíficoLimaPeru

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