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Developing Artwork Pricing Models for Online Art Sales Using Text Analytics

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Rough Sets (IJCRS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11499))

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Abstract

This work explores utilizing a combination of features, built with text analytics, and other features to predict prices of works of art. Basic metrics, such as the length of the text descriptions and the presence of the artist’s social media links are considered as attributes for predicting the price of art. This work also utilizes the Paragraph2Vec algorithm combined with clustering as a method of classifying artworks for price.

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References

  1. Beautiful Soup. https://www.crummy.com/software/BeautifulSoup/

  2. Selenium. https://www.seleniumhq.org/

  3. New directions in sentiment analysis: charting words. In: Sentiment Indicators, pp. 227–250. Wiley, October 2015. https://doi.org/10.1002/9781119204398.ch12

  4. 2015/2016 The Shotfarm Product Information Report. Technical report (2016)

    Google Scholar 

  5. The Hiscox Online Art Trade Report 2018. Technical report, ArtTactic (2018). https://arttactic.com/product/hiscox-online-art-trade-report-2018/

  6. Artfinder.com (2019). https://www.artfinder.com/

  7. Saatchiart.com (2019). https://www.saatchiart.com/

  8. 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

  9. Barkan, O., Koenigstein, N.: Item2Vec: neural item embedding for collaborative filtering (2016). arXiv:1603.04259v3

  10. Beckert, J., Rössel, J.: The price of art: uncertainty and reputation in the art field. Eur. Soc. 15(2), 178–195 (2013)

    Article  Google Scholar 

  11. 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)

    Google Scholar 

  12. Dai, A.M., Olah, C., Le, Q.V.: Document embedding with paragraph vectors (2015). arXiv:1507.07998v1

  13. Dara, S., Chowdary, C.R., Kumar, C.: A survey on group recommender systems. J. Intell. Inf. Syst. (2019). https://doi.org/10.1007/s10844-018-0542-3

  14. 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)

    Article  Google Scholar 

  15. Demšar, J., et al.: Orange: data mining toolbox in Python. J. Mach. Learn. Res. 14, 2349–2353 (2013)

    MATH  Google Scholar 

  16. Evans, D.: The current and future influence of online art sales on the art market. Ph.D. thesis (2015)

    Google Scholar 

  17. Felfernig, A., et al.: An overview of recommender systems in the Internet of Things. J. Intell. Inf. Syst. 52(2), 285–309 (2019)

    Article  Google Scholar 

  18. Fischer, M.S.: Online Art Sales Gathers Steam Among Buyers. ThinkAdvisor, April 2015

    Google Scholar 

  19. de Fortuny, E.J., Smedt, T.D., Martens, D., Daelemans, W.: Evaluating and understanding text-based stock price prediction models. Inf. Process. Manag. 50(2), 426–441 (2014)

    Article  Google Scholar 

  20. Guo, L., Liang, J., Zhu, Y., Luo, Y., Sun, L., Zheng, X.: Collaborative filtering recommendation based on trust and emotion. J. Intell. Inf. Syst. (2018). https://doi.org/10.1007/s10844-018-0517-4

  21. Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents (2014). arXiv:1405.4053v2

  22. Lee, H., Yoon, Y.: Engineering doc2vec for automatic classification of product descriptions on O2O applications. Electron. Commer. Res. 18(3), 433–456 (2018)

    Article  Google Scholar 

  23. Li, J., Xu, Z., Yu, L., Tang, L.: Forecasting oil price trends with sentiment of online news articles. Procedia Comput. Sci. 91, 1081–1087 (2016)

    Article  Google Scholar 

  24. Mardini, M.T., Raś, Z.W.: Extraction of actionable knowledge to reduce hospital readmissions through patients personalization. Inf. Sci. 485, 1–17 (2019)

    Article  Google Scholar 

  25. Mendoza, M., Torres, N.: Evaluating content novelty in recommender systems. J. Intell. Inf. Syst. (2019). https://doi.org/10.1007/s10844-019-00548-x

  26. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv:1301.3781v3

  27. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality (2013). arXiv:1310.4546v1

  28. Nobahari, V., Jalali, M., Seyyed Mahdavi, S.J.: ISoTrustSeq: a social recommender system based on implicit interest, trust and sequential behaviors of users using matrix factorization. J. Intell. Inf. Syst. 52(2), 239–268 (2019)

    Article  Google Scholar 

  29. Parish, S.: Product Description Word Counts: Why Length Matters. https://content26.com/blog/product-description-word-counts-length-matters-2/

  30. 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. SBD, vol. 40, pp. 41–52. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77604-0_4

    Chapter  Google Scholar 

  31. Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)

    Article  Google Scholar 

  32. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  33. Ras, Z.W., Tarnowska, K.A., Kuang, J., Daniel, L., Fowler, D.: User friendly NPS-based recommender system for driving business revenue. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10313, pp. 34–48. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60837-2_4

    Chapter  Google Scholar 

  34. 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 

  35. 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, May 2010

    Google Scholar 

  36. Tarnowska, K., Ras, Z.W., Daniel, L.: Recommender System for Improving Customer Loyalty. SBD, vol. 55. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-13438-9

    Book  Google Scholar 

  37. Trang Tran, T.N., Atas, M., Felfernig, A., Stettinger, M.: An overview of recommender systems in the healthy food domain. J. Intell. Inf. Syst. 50(3), 501–526 (2018)

    Article  Google Scholar 

  38. Tseng, M.Y.: Describing creative products in an intercultural context: toward a pragmatic and empirical account. J. Pragmat. 80, 52–69 (2015)

    Article  Google Scholar 

  39. 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, September 2015. https://doi.org/10.1109/icecco.2015.7416902

  40. Zheng, X., Luo, Y., Sun, L., Zhang, J., Chen, F.: A tourism destination recommender system using users’ sentiment and temporal dynamics. J. Intell. Inf. Syst. 51(3), 557–578 (2018)

    Article  Google Scholar 

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Acknowledgement

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.

Author information

Authors and Affiliations

  1. College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA

    Laurel Powell, Anna Gelich & Zbigniew W. Ras

  2. New Media Arts Department, Polish-Japanese Academy of Information Technology, 02-008, Warsaw, Poland

    Anna Gelich

  3. Computer Science Department, Polish-Japanese Academy of Information Technology, 02-008, Warsaw, Poland

    Zbigniew W. Ras

Authors
  1. Laurel Powell
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  2. Anna Gelich
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  3. Zbigniew W. Ras
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Corresponding author

Correspondence to Laurel Powell .

Editor information

Editors and Affiliations

  1. University of Debrecen, Debrecen, Hungary

    Tamás Mihálydeák

  2. Southwest Petroleum University, Chengdu, China

    Fan Min

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Mohua Banerjee

  5. Fujian Normal University, Fuzhou, China

    Ivo Düntsch

  6. University of Rzeszów, Rzeszow, Poland

    Zbigniew Suraj

  7. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

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Powell, L., Gelich, A., Ras, Z.W. (2019). Developing Artwork Pricing Models for Online Art Sales Using Text Analytics. In: Mihálydeák, T., et al. Rough Sets. IJCRS 2019. Lecture Notes in Computer Science(), vol 11499. Springer, Cham. https://doi.org/10.1007/978-3-030-22815-6_37

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  • DOI: https://doi.org/10.1007/978-3-030-22815-6_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22814-9

  • Online ISBN: 978-3-030-22815-6

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