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An Article-Oriented Framework for Automatic Semantic Analysis of COVID-19 Researches

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12951))

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Abstract

In this work, we propose an article-oriented framework for automatically extract semantic topics from scientific articles related to COVID-19 researches. Our framework has four key building blocks (i) pre-processing, (ii) topic modeling, (iii) correlating topics, authors, and institutions, and (iv) a summarization interface. The first one corresponds to apply traditional textual pre-processing strategies in the texts extracted from articles and constructing their data representations. The topic modeling block aims at finding semantic topics from articles based on the most relevant words of each discovered topic. The third block correlates these discovered topics with the articles, authors (researchers), and institutions. The summarization interface provides an intuitive visualization for these results. Our evaluation shows that our framework can automatically extract relevant features from the articles, identifying the key topics covered by them, as well as the contribution of researchers, institutions, and countries to the topics. Our framework can help research institutions and companies to form multidisciplinary teams and funding agencies to identify more promising research approaches regarding COVID-19.

Supported by CAPES, CNPq, Finep, Fapesp and Fapemig.

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Notes

  1. 1.

    https://scholar.google.com.

  2. 2.

    username: user, password: covid-19.

  3. 3.

    https://github.com/explosion/spaCy/blob/master/spacy/lang/en/stop_words.py.

  4. 4.

    https://www.nltk.org/api/nltk.tokenize.html#module-nltk.tokenize.casual.

  5. 5.

    labpi.ufsj.edu.br/covpapers/

    username: user

    password: covid-19.

  6. 6.

    https://www.erasmusmc.nl/en/research/researchers/koopmans-marion.

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Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and National Council for Scientific and Technological Development – CNPq.

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Authors and Affiliations

  1. Universidade Federal de São João del-Rei, São João del Rei, Brazil

    Antonio Pedro, Antônio Pereira, Pablo Cecilio, Nayara Pena, Elisa Tuler, Diego Dias & Leonardo Rocha

  2. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Felipe Viegas

Authors
  1. Antonio Pedro
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  2. Antônio Pereira
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  3. Pablo Cecilio
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  4. Nayara Pena
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  5. Felipe Viegas
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  6. Elisa Tuler
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  7. Diego Dias
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  8. Leonardo Rocha
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Corresponding author

Correspondence to Antonio Pedro .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Pedro, A. et al. (2021). An Article-Oriented Framework for Automatic Semantic Analysis of COVID-19 Researches. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12951. Springer, Cham. https://doi.org/10.1007/978-3-030-86970-0_13

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  • DOI: https://doi.org/10.1007/978-3-030-86970-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86969-4

  • Online ISBN: 978-3-030-86970-0

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