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Investigating the Sentiment in Italian Long-COVID Narrations

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Computational Science – ICCS 2023 (ICCS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14074))

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Abstract

Through an overview of the history of the disease, Narrative Medicine (NM) aims to define and implement an effective, appropriate and shared treatment path. In the context of COVID-19, several blogs were produced, among those the “Sindrome Post COVID-19” contains narratives related to the COVID-19 pandemic. In the present study, different analysis techniques were applied to a dataset extracted from such “Sindrome Post COVID-19” blog. The first step of the analysis was to test the VADER polarity extraction tool. Then the analysis was extended through the application of Topic Modeling, using Latent Dirichlet Allocation (LDA).

The results were compared to verify the correlations between the polarity score obtained through VADER and the extracted topics through LDA. The results showed a predominantly negative polarity consistent with the mostly negative topics represented by words on post virus symptoms. The results obtained derive from three different approaches applied to the COVID narrative dataset. The first part of the analysis corresponds to polarity extraction using the VADER software, where, from the score, polarity was inferred by dichotomizing the overall score. In the second part, topic modeling through LDA was applied, extracting a number of topics equal to three. The third phase is based on the objective of finding a qualitative relationship between the polarity extracted with VADER and the latent topics with LDA, considering it a semi-supervised problem. In the end, the presence of polarized topics was explored and thus a correspondence between sentiment and topic was found.

M. C. Martinis and I. Scarpino—Contributed equally to this work.

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Notes

  1. 1.

    https://www.sindromepostcovid19.it/.

  2. 2.

    https://www.sindromepostcovid19.it/.

  3. 3.

    https://github.com/cjhutto/vaderSentiment.

  4. 4.

    https://github.com/topics/lda-topic-modeling.

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Authors and Affiliations

  1. Data Analytics Research Center, Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy

    Maria Chiara Martinis, Ileana Scarpino, Chiara Zucco & Mario Cannataro

Authors
  1. Maria Chiara Martinis
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  2. Ileana Scarpino
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  3. Chiara Zucco
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  4. Mario Cannataro
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Corresponding authors

Correspondence to Maria Chiara Martinis or Ileana Scarpino .

Editor information

Editors and Affiliations

  1. Czech Technical University in Prague, Prague, Czech Republic

    Jiří Mikyška

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Martinis, M.C., Scarpino, I., Zucco, C., Cannataro, M. (2023). Investigating the Sentiment in Italian Long-COVID Narrations. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_65

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  • DOI: https://doi.org/10.1007/978-3-031-36021-3_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36020-6

  • Online ISBN: 978-3-031-36021-3

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