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Performance of Machine Learning Methods Using Tweets

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Mathematical Methods for Engineering Applications (ICMASE 2022)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 414))

  • 308 Accesses

Abstract

Public opinions shared in common platforms like Twitter, Facebook, Instagram, etc. act as the sources of information for experts. Transportation and analysis of such data is very important and difficult due to data regulations and its structure. The pre-processing approaches and word-based dictionaries are used to understand the unprocessed data and make possible the opinions/tweets to be analyzed. Machine learning algorithms learn from past experience and use a variety of statistical, probabilistic and optimization algorithms to detect useful patterns from unstructured data sets. Our study aims to compare the performance of classification algorithms to predict individuals with COVID-19(\(+\)) or COVID-19(−) using the emotions among the tweets by text mining procedures. Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Trees (DT), Random Forest (RF), Artificial Neural Networks (ANN), Gradient Boost (GBM) and XGradient algorithms were used to extract the accuracy of model performance of each model for the detection and identification of the disease related to the COVID-19 virus, which has been on the agenda recently.

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Acknowledgement

This work was supported by Eskişehir Technical University Scientific Projects Commissions under the grade no 22ADP026.

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Authors and Affiliations

  1. Department of Statistics, Faculty of Science, Eskisehir Technical University, 26470, Eskisehir, Turkey

    İlkay Tuğ & Betül Kan-Kilinç

Authors
  1. İlkay Tuğ
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  2. Betül Kan-Kilinç
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Corresponding author

Correspondence to Betül Kan-Kilinç .

Editor information

Editors and Affiliations

  1. Faculty of Arts and Sciences, Ankara Hacı Bayram Veli University, Polatli, Ankara, Türkiye

    Fatih Yilmaz

  2. Department of Applied Mathematics, University of Salamanca, Salamanca, Spain

    Araceli Queiruga-Dios

  3. Department of Applied Mathematics, University of Salamanca, Salamanca, Spain

    Jesús Martín Vaquero

  4. Department of Mathematics and Computer Science, Technical University of Civil Engineering, Bucharest, Romania

    Ion Mierluş-Mazilu

  5. Department of Physics and Mathematics, Instituto Superior de Engenharia de Coimbra, Coimbra, Portugal

    Deolinda Rasteiro

  6. Centro Universitario de Tecnología y Arte Digital (U-tad) , Las Rozas de Madrid, Spain

    Víctor Gayoso Martínez

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Tuğ, İ., Kan-Kilinç, B. (2023). Performance of Machine Learning Methods Using Tweets. In: Yilmaz, F., Queiruga-Dios, A., Martín Vaquero, J., Mierluş-Mazilu, I., Rasteiro, D., Gayoso Martínez, V. (eds) Mathematical Methods for Engineering Applications. ICMASE 2022. Springer Proceedings in Mathematics & Statistics, vol 414. Springer, Cham. https://doi.org/10.1007/978-3-031-21700-5_13

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