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.
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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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Powell, L., Gelich, A., Ras, Z.W. (2020). Applying Analytics to Artist Provided Text to Model Prices of Fine Art. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) Complex Pattern Mining. Studies in Computational Intelligence, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-36617-9_12
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