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Method of Distinguishing Styles by Fractal and Statistical Indicators of the Text as a Sequence of the Number of Letters in Its Words

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  • Roman Kaminskiy

    (Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Nataliya Shakhovska

    (Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Jana Kajanová

    (Department of Information Systems, Faculty of Management, Comenius University, Odbojárov 10, 814 99 Bratislava, Slovakia)

  • Yurii Kryvenchuk

    (Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

Abstract

The method of the analysis of different texts styles is developed in the paper. Integer numerical sequences are used as models. The elements of the sequence are the number of letters in the words of the text. The algorithm for calculating the exact value of the fractal dimension is developed. It provides the determination of the exact value of the Hurst index. The value of the power dependence constant is calculated. The obtained indicators in the aspect of fractality completely describe the objects of research.

Suggested Citation

  • Roman Kaminskiy & Nataliya Shakhovska & Jana Kajanová & Yurii Kryvenchuk, 2021. "Method of Distinguishing Styles by Fractal and Statistical Indicators of the Text as a Sequence of the Number of Letters in Its Words," Mathematics, MDPI, vol. 9(19), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2410-:d:644820
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    References listed on IDEAS

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    1. Ashlee Humphreys & Rebecca Jen-Hui Wang & Eileen FischerEditor & Linda PriceAssociate Editor, 2018. "Automated Text Analysis for Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(6), pages 1274-1306.
    2. Liu, Yao & Wang, Yashun & Chen, Xun & Zhang, Chunhua & Tan, Yuanyuan, 2017. "Two-stage method for fractal dimension calculation of the mechanical equipment rough surface profile based on fractal theory," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 495-502.
    3. Wu, Xingli & Liao, Huchang, 2019. "A consensus-based probabilistic linguistic gained and lost dominance score method," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1017-1027.
    4. Bana Abuzayed & Nedal Al-Fayoumi & Lanouar Charfeddine, 2018. "Long range dependence in an emerging stock market’s sectors: volatility modelling and VaR forecasting," Applied Economics, Taylor & Francis Journals, vol. 50(23), pages 2569-2599, May.
    5. Witold Orzeszko, 2010. "Fractal dimension of time series as a measure of investment risk," Acta Universitatis Nicolai Copernici, Ekonomia, Uniwersytet Mikolaja Kopernika, vol. 41, pages 57-70.
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    1. Evgeniya Gospodinova & Penio Lebamovski & Galya Georgieva-Tsaneva & Galina Bogdanova & Diana Dimitrova, 2022. "Methods for Mathematical Analysis of Simulated and Real Fractal Processes with Application in Cardiology," Mathematics, MDPI, vol. 10(19), pages 1-16, September.

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