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Crime Alert! Crime Typification in News Based on Text Mining

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Advances in Information and Communication (FICC 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 69))

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Abstract

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.

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Notes

  1. 1.

    Peruvian National Statistics and Informatics Institute.

  2. 2.

    iMedia website: www.imedia.pe.

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Acknowledgements

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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Authors and Affiliations

  1. Universidad del Pacífico, Av. Salaverry, 2020, Lima, Peru

    Hugo Alatrista-Salas, Juandiego Morzán-Samamé & Miguel Nunez-del-Prado

Authors
  1. Hugo Alatrista-Salas
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  2. Juandiego Morzán-Samamé
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  3. Miguel Nunez-del-Prado
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Corresponding author

Correspondence to Miguel Nunez-del-Prado .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Alatrista-Salas, H., Morzán-Samamé, J., Nunez-del-Prado, M. (2020). Crime Alert! Crime Typification in News Based on Text Mining. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-12388-8_50

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