Applying Analytics to Artist Provided Text to Model Prices of Fine Art

  • Laurel PowellEmail author
  • Anna Gelich
  • Zbigniew W. Ras
Part of the Studies in Computational Intelligence book series (SCI, volume 880)


This work develops a set of text based features to be used in the prediction of the price of a work of contemporary art sold online. These features are developed using text clustering based on vectors and sentiment analysis. These features are then examined for their impact on the accuracy of a predictive model.


Data analytics Art market Sentiment analysis 



This research is supported by the National Science Foundation under grant IIP 1749105. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Laurel Powell
    • 1
    Email author
  • Anna Gelich
    • 1
    • 2
  • Zbigniew W. Ras
    • 1
    • 3
  1. 1.University of North Carolina at CharlotteCharlotteUSA
  2. 2.New Media Arts DepartmentPolish-Japanese Academy of Information TechnologyWarsawPoland
  3. 3.Computer Science DepartmentPolish-Japanese Academy of Information TechnologyWarsawPoland

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