Crime alert! crime typification in news based on text mining

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

doi:10.1007/978-3-030-12388-8_50

Crime alert! crime typification in news based on text mining

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

Universidad del Pacífico

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

8 Scopus citations

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.

Original language	English
Title of host publication	Advances in information and communication
Subtitle of host publication	Proceedings of the 2019 Future of Information and Communication Conference (FICC)
Editors	Kohei Arai, Rahul Bhatia
Place of Publication	Springer, Cham
Publisher	Springer Verlag
Pages	725-741
Number of pages	17
Volume	1
ISBN (Electronic)	9783030123888
ISBN (Print)	9783030123871
DOIs	https://doi.org/10.1007/978-3-030-12388-8_50
State	E-pub ahead of print - 2 Feb 2019
Event	Future of Information and Communication Conference (FICC) 2019 - San Francisco, United States Duration: 14 Mar 2019 → 15 Mar 2019 https://saiconference.com/Conferences/FICC2019

Publication series

Name	Lecture Notes in Networks and Systems
Volume	69

Conference

Conference	Future of Information and Communication Conference (FICC) 2019
Country/Territory	United States
City	San Francisco
Period	14/03/19 → 15/03/19
Internet address	https://saiconference.com/Conferences/FICC2019

Bibliographical note

Publisher Copyright: © Springer Nature Switzerland AG 2020.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions
SDG 17 Partnerships for the Goals

Keywords

Classification
Crime analysis
Text mining
Word vectorization and embeddings

Access to Document

10.1007/978-3-030-12388-8_50

Cite this

Alatrista-Salas, H., Morzán-Samamé, J., & Nunez-del-Prado, M. (2019). Crime alert! crime typification in news based on text mining. In K. Arai, & R. Bhatia (Eds.), Advances in information and communication: Proceedings of the 2019 Future of Information and Communication Conference (FICC) (Vol. 1, pp. 725-741). (Lecture Notes in Networks and Systems; Vol. 69). Springer Verlag. Advance online publication. https://doi.org/10.1007/978-3-030-12388-8_50

Alatrista-Salas, H. ; Morzán-Samamé, J. ; Nunez-del-Prado, M. / Crime alert! crime typification in news based on text mining. Advances in information and communication: Proceedings of the 2019 Future of Information and Communication Conference (FICC). editor / Kohei Arai ; Rahul Bhatia. Vol. 1 Springer, Cham : Springer Verlag, 2019. pp. 725-741 (Lecture Notes in Networks and Systems).

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Alatrista-Salas, H, Morzán-Samamé, J & Nunez-del-Prado, M 2019, Crime alert! crime typification in news based on text mining. in K Arai & R Bhatia (eds), Advances in information and communication: Proceedings of the 2019 Future of Information and Communication Conference (FICC). vol. 1, Lecture Notes in Networks and Systems, vol. 69, Springer Verlag, Springer, Cham, pp. 725-741, Future of Information and Communication Conference (FICC) 2019, San Francisco, California, United States, 14/03/19. https://doi.org/10.1007/978-3-030-12388-8_50

Crime alert! crime typification in news based on text mining. / Alatrista-Salas, H.; Morzán-Samamé, J.; Nunez-del-Prado, M.
Advances in information and communication: Proceedings of the 2019 Future of Information and Communication Conference (FICC). ed. / Kohei Arai; Rahul Bhatia. Vol. 1 Springer, Cham: Springer Verlag, 2019. p. 725-741 (Lecture Notes in Networks and Systems; Vol. 69).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Alatrista-Salas, H.

AU - Morzán-Samamé, J.

AU - Nunez-del-Prado, M.

N1 - Publicado Proceedings de la conferencia: 2020. Publisher Copyright: © Springer Nature Switzerland AG 2020.

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AB - 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.

KW - Classification

KW - Crime analysis

KW - Text mining

KW - Word vectorization and embeddings

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SN - 9783030123871

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SP - 725

EP - 741

BT - Advances in information and communication

A2 - Arai, Kohei

A2 - Bhatia, Rahul

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CY - Springer, Cham

T2 - Future of Information and Communication Conference (FICC) 2019

Y2 - 14 March 2019 through 15 March 2019

ER -

Alatrista-Salas H, Morzán-Samamé J, Nunez-del-Prado M. Crime alert! crime typification in news based on text mining. In Arai K, Bhatia R, editors, Advances in information and communication: Proceedings of the 2019 Future of Information and Communication Conference (FICC). Vol. 1. Springer, Cham: Springer Verlag. 2019. p. 725-741. (Lecture Notes in Networks and Systems). Epub 2019 Feb 2. doi: 10.1007/978-3-030-12388-8_50

Crime alert! crime typification in news based on text mining

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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Cite this