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Visual Analytics-Based Method for Sentiment Analysis of COVID-19 Ukrainian Tweets

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Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making (ISDMCI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 149))

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Abstract

The coronavirus pandemic is one of the leading communication topics for users on social networks. It causes different emotions in people: fear, sadness, anger, joy, and elation. Detecting sentiment about the pandemic is an acute challenge because it helps track people’s attitudes about the pandemic itself and the messages and decisions of local authorities aimed at combating the coronavirus. To address the issue, namely natural language processing, messages are processed using the TextRank vectorization method and the SVM-based two-level classification model. The first stage is the detection of tweets that are directly related to the coronavirus. The second stage means detecting the sentiment of the dataset obtained in the first stage. The classifier’s effectiveness was tested using the following metrics: precision, recall, F1-norm, and confusion matrix, and averaged about 90%. Thus, the automated detection of the sentiment of Twitter messages about the coronavirus pandemic was obtained. The approach described in the paper will allow assessing public opinion on pandemic control measures applied by the country’s governments.

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Author information

Authors and Affiliations

  1. Khmelnytskyi National University, Khmelnytskyi, Ukraine

    Oleksii Kovalchuk, Vitalii Slobodzian, Olena Sobko, Maryna Molchanova, Olexander Mazurets & Oleksander Barmak

  2. Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

    Iurii Krak

  3. Glushkov Institute of Cybernetics of NAS of Ukraine, Kyiv, Ukraine

    Iurii Krak

  4. National University of Water and Environmental Engineering, Rivne, Ukraine

    Nataliia Savina

Authors
  1. Oleksii Kovalchuk
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  2. Vitalii Slobodzian
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  3. Olena Sobko
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  4. Maryna Molchanova
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  5. Olexander Mazurets
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  6. Oleksander Barmak
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  7. Iurii Krak
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  8. Nataliia Savina
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Corresponding author

Correspondence to Iurii Krak .

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Editors and Affiliations

  1. Jan Evangelista Purkyně University in Ústi nad Labem, Ústi nad Labem, Czech Republic

    Sergii Babichev

  2. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Kovalchuk, O. et al. (2023). Visual Analytics-Based Method for Sentiment Analysis of COVID-19 Ukrainian Tweets. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_33

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