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Aspect-Based Opinion Mining in Drug Reviews

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Progress in Artificial Intelligence (EPIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10423))

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Abstract

Aspect-based opinion mining can be applied to extract relevant information expressed by patients in drug reviews (e.g., adverse reactions, efficacy of a drug, symptoms and conditions of patients). This new domain of application presents challenges as well as opportunities for research in opinion mining. Nevertheless, the literature is still scarce of methods to extract multiple relevant aspects present in drug reviews. In this paper we propose a method to extract and classify aspects in drug reviews. The proposed solution has two main steps. In the aspect extraction, a method based on syntactic dependency paths is proposed to extract opinion pairs in drug reviews, composed by an aspect term associated to a sentiment modifier. In the aspect classification, a supervised classification is proposed based on domain and linguistics resources to classify the opinion pairs by aspect type (e.g., condition, adverse reaction, dosage and effectiveness). In order to evaluate the proposed method we conducted experiments with datasets related to three different diseases: ADHD, AIDS and Anxiety. Promising results were obtained in the experiments and various issues were identified and discussed.

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Notes

  1. 1.

    https://stanfordnlp.github.io/CoreNLP/.

  2. 2.

    https://code.google.com/archive/p/google-api-spelling-java/.

  3. 3.

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

  4. 4.

    https://metamap.nlm.nih.gov.

  5. 5.

    sideeffects.embl.de/.

  6. 6.

    https://sourceforge.net/projects/ohnlp/files/MedTagger/.

  7. 7.

    https://code.google.com/archive/p/negex/.

  8. 8.

    http://www.cs.waikato.ac.nz/ml/weka/.

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Author information

Authors and Affiliations

  1. Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, Brazil

    Diana Cavalcanti & Ricardo Prudêncio

Authors
  1. Diana Cavalcanti
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  2. Ricardo Prudêncio
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Corresponding author

Correspondence to Diana Cavalcanti .

Editor information

Editors and Affiliations

  1. Universidade do Porto, Porto, Portugal

    Eugénio Oliveira

  2. Universidade do Porto, Porto, Portugal

    João Gama

  3. Polytechnic Institute of Porto, Porto, Portugal

    Zita Vale

  4. Universidade do Porto, Porto, Portugal

    Henrique Lopes Cardoso

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Cavalcanti, D., Prudêncio, R. (2017). Aspect-Based Opinion Mining in Drug Reviews. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_66

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  • DOI: https://doi.org/10.1007/978-3-319-65340-2_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65339-6

  • Online ISBN: 978-3-319-65340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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