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Lexicon-Based System for Drug Abuse Entity Extraction from Twitter

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Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery (BDAS 2015, BDAS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 613))

Abstract

Drug abuse and addiction is a serious healthcare problem and social phenomenon that has not received the interest deserved in scientific research due to the lack of information. Today, social media have become an ubiquitous source of information in this field since they are the environment on which addicted individuals rely to talk about their dependencies. However, extracting salient information from social media is a difficult task regarding their noisy, dynamic and unstructured character. In addition, natural language processing tools (NLP) are not conceived to manage social data and cannot extract semantic and domain-specific entities.

In this paper, we propose a framework for real time collection and analysis of Twitter data which heart is a personalized NLP process for the extraction of drug abuse information. We extend Stanford CoreNLP pipeline with a customized annotator based on fuzzy matching with drug abuse and addiction lexicons in a dictionary. Our system, ran on 86 041 tweets, achieved 82 % of accuracy.

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Notes

  1. 1.

    www.Twitter.com.

  2. 2.

    https://www.nlm.nih.gov/research/umls/.

  3. 3.

    http://knoesis-hpco.cs.wright.edu/predose/ontologies/DAO.owl.

  4. 4.

    http://consumerhealthvocab.org/.

  5. 5.

    http://www.fda.gov/.

  6. 6.

    http://www.aemps.gob.es/cima.

  7. 7.

    http://www.meddra.org/.

  8. 8.

    http://cs.nyu.edu/faculty/grishman/muc6.

  9. 9.

    http://www.drugabuse.gov.

  10. 10.

    http://www.who.int.

  11. 11.

    http://www.drugabuse.gov/.

  12. 12.

    http://www.drugbank.ca/.

  13. 13.

    https://www.noslang.com/drugs/dictionary.

  14. 14.

    http://consumerhealthvocab.org.

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Authors and Affiliations

  1. SOIE Laboratory, Institut Suprieur de Gestion de Tunis, Rue de la Liberte. Bouchoucha, Tunis, Tunisia

    Ferdaous Jenhani, Mohamed Salah Gouider & Lamjed Ben Said

Authors
  1. Ferdaous Jenhani
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  2. Mohamed Salah Gouider
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  3. Lamjed Ben Said
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Corresponding author

Correspondence to Ferdaous Jenhani .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Jenhani, F., Gouider, M.S., Said, L.B. (2016). Lexicon-Based System for Drug Abuse Entity Extraction from Twitter. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_54

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  • DOI: https://doi.org/10.1007/978-3-319-34099-9_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34098-2

  • Online ISBN: 978-3-319-34099-9

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