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Deep Analytics Based on Triathlon Athletes’ Blogs and News

  • Iztok FisterJr.
  • Dušan Fister
  • Samo Rauter
  • Uroš Mlakar
  • Janez Brest
  • Iztok Fister
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 837)

Abstract

Studying the lifestyle of various groups of athletes has been a very interesting research direction of many social sport scientists. Following the behavior of these athletes’ groups might reveal how they work, yet function in the real-world. Triathlon is basically depicted as one of the hardest sports in the world (especially long-distance triathlons). Hence, studying this group of people can have a very positive influence on designing new perspectives and theories about their lifestyle. Additionally, the discovered information also helps in designing modern systems for planning sport training sessions. In this paper, we apply deep analytic methods for discovering knowledge from triathlon athletes’ blogs and news posted on their websites. Practical results reveal that triathlon remains in the forefront of the athletes’ minds through the whole year.

Keywords

Artificial Sport Trainer Data mining Data science Lifestyle Triathletes Websites 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Iztok FisterJr.
    • 1
  • Dušan Fister
    • 2
  • Samo Rauter
    • 3
  • Uroš Mlakar
    • 1
  • Janez Brest
    • 1
  • Iztok Fister
    • 1
  1. 1.Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMariborSlovenia
  2. 2.Faculty of Mechanical EngineeringUniversity of MariborMariborSlovenia
  3. 3.Faculty of SportUniversity of LjubljanaLjubljanaSlovenia

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