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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CITATION STYLE
Powell, L., Gelich, A., & Ras, Z. W. (2019). Developing Artwork Pricing Models for Online Art Sales Using Text Analytics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11499 LNAI, pp. 480–494). Springer Verlag. https://doi.org/10.1007/978-3-030-22815-6_37
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