Advertisement

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

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

Abstract

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.

Keywords

Data analytics Art market Sentiment analysis 

Notes

Acknowledgements

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.

References

  1. 1.
    2015/2016 The Shotfarm Product Information Report. Technical report (2016)Google Scholar
  2. 2.
    Aggarwal, C.C.: Machine Learning for Text. Springer International Publishing, Cham (2018).  https://doi.org/10.1007/978-3-319-73531-3CrossRefGoogle Scholar
  3. 3.
    Artfinder.com (2019) https://www.artfinder.com/
  4. 4.
    Bamberger, A.: How artists use instagram to present and sell their art. https://www.artbusiness.com/artists-how-to-use-post-sell-art-on-instagram.html
  5. 5.
    Barkan, O., Koenigstein, N.: Item2vec: neural item embedding for collaborative filtering (2016). arXiv:1603.04259v3
  6. 6.
  7. 7.
    Beckert, J., Rssel, J.: The price of art: uncertainty and reputation in the art field. Eur. Soc. 15(2), 178–195 (2013). https://doi.org/10.1080/14616696.2013.767923CrossRefGoogle Scholar
  8. 8.
    Beysolow II, T.: Topic modeling and word embeddings. In: Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing, pp. 77–119. Apress, Berkeley (2018). https://doi.org/10.1007/978-1-4842-3733-5_4CrossRefGoogle Scholar
  9. 9.
    Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python, 1st edn. O’Reilly Media, Inc., Newton (2009)Google Scholar
  10. 10.
    Dai, A.M., Olah, C., Le, Q.V.: Document embedding with paragraph vectors (2015). arXiv:1507.07998v1
  11. 11.
    Dass, M., Reddy, S.K., Iacobucci, D.: A network bidder behavior model in online auctions: a case of fine art auctions. J. Retail. 90(4), 445–462 (2014). https://doi.org/10.1016/j.jretai.2014.08.003CrossRefGoogle Scholar
  12. 12.
    Demšar, J., Curk, T., Erjavec, A., Črt Gorup, Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., Zupan, B.: Orange: data mining toolbox in python. J. Mach. Learn. Res. 14, 2349–2353 (2013). http://jmlr.org/papers/v14/demsar13a.html
  13. 13.
    Hutto, C., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014 (2015)Google Scholar
  14. 14.
    Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents (2014). arXiv:1405.4053v2
  15. 15.
    Lee, H., Yoon, Y.: Engineering doc2vec for automatic classification of product descriptions on O2o applications. Electron. Commer. Res. 18(3), 433–456 (2018). https://doi.org/10.1007/s10660-017-9268-5CrossRefGoogle Scholar
  16. 16.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv:1301.3781v3
  17. 17.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality (2013). arXiv:1310.4546v1
  18. 18.
    Montoyo, A., Martnez-Barco, P., Balahur, A.: Subjectivity and sentiment analysis: an overview of the current state of the area and envisaged developments. Decis. Support Syst. 53(4), 675–679 (2012).  https://doi.org/10.1016/j.dss.2012.05.022, https://linkinghub.elsevier.com/retrieve/pii/S0167923612001339CrossRefGoogle Scholar
  19. 19.
    Parish, S.: Product description word counts: why length matters. https://content26.com/blog/product-description-word-counts-length-matters-2/
  20. 20.
    Pawlowski, C., Gelich, A., Raś, Z.W.: Can we build recommender system for artwork evaluation? In: Bembenik, R., Skonieczny, Ł., Protaziuk, G., Kryszkiewicz, M., Rybinski, H. (eds.) Intelligent Methods and Big Data in Industrial Applications, pp. 41–52. Springer International Publishing, Cham (2019).  https://doi.org/10.1007/978-3-319-77604-0_4Google Scholar
  21. 21.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Powell, L., Gelich, A., Ras, Z.W.: Developing artwork pricing models for online art sales using text analytics. In: Mihlydek, T., Min, F., Wang, G., Banerjee, M., Dntsch, I., Suraj, Z., Ciucci, D. (eds.) Rough Sets, pp. 480–494. Springer International Publishing, Cham (2019)CrossRefGoogle Scholar
  23. 23.
    Rawlins, C., Johnson, P.: Selling on ebay: persuasive communication advice based on analysis of auction item descriptions. J. Strat. E-Commer. 5(1&2), 75–81 (2007)Google Scholar
  24. 24.
    Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. pp. 45–50. ELRA, Valletta, Malta (2010), http://is.muni.cz/publication/884893/en
  25. 25.
    Renneboog, L., Spaenjers, C.: Buying beauty: on prices and returns in the art market. Manag. Sci. 59(1), 36–53 (2013).  https://doi.org/10.1287/mnsc.1120.1580CrossRefGoogle Scholar
  26. 26.
    Saatchiart.com (2019) https://www.saatchiart.com/
  27. 27.
  28. 28.
    The Hiscox Online Art Trade Report 2018. Technical report, ArtTactic (2018). https://arttactic.com/product/hiscox-online-art-trade-report-2018/
  29. 29.
    Tseng, M.Y.: Describing creative products in an intercultural context: toward a pragmatic and empirical account. J. Pragmat. 80, 52–69 (2015).  https://doi.org/10.1016/j.pragma.2015.02.004CrossRefGoogle Scholar
  30. 30.
    Velthuis, O.: Talking Prices: Symbolic Meanings of Prices on the Market for Contemporary Art. Princeton University Press, Princeton (2005). http://www.jstor.org/stable/j.ctt4cgd14
  31. 31.
    Zharmagambetov, A.S., Pak, A.A.: Sentiment analysis of a document using deep learning approach and decision trees. In: 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO). IEEE (2015).  https://doi.org/10.1109/icecco.2015.7416902

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

Personalised recommendations