Crime alert! crime typification in news based on text mining

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

Research output: Chapter in Book/Report/Conference proceedingChapter

2 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 languageEnglish
Title of host publicationLecture Notes in Networks and Systems
Subtitle of host publicationProceedings of the 2019 Future of Information and Communication Conference
EditorsKohei Arai, Rahul Bhatia
Place of PublicationCham
PublisherSpringer Verlag
Pages725-741
Number of pages17
ISBN (Electronic)9783030123888
ISBN (Print)9783030123871
DOIs
StatePublished - 2020

Publication series

NameLecture Notes in Networks and Systems
Volume69

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

  • Classification
  • Crime analysis
  • Text mining
  • Word vectorization and embeddings

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