Factors of Price Formation for Art Objects With the Application of Text Analysis of Twitter News
https://doi.org/10.33293/1609-1442-2020-2(89)-114-131
Abstract
This work confirmed the hypotheses about the influence of the mood index on Twitter on the pricing of art objects and the difference between the experts' estimations and the final price of the auction. The hypotheses were tested with the use of a sample of 83 paintings selected on the basis of ratings of ARTNET's online resource about the most expensive works of art ever sold in the last 10–15 years. The sample consisted of 25 artists, for each of them was made an index of moods on Twitter. This index was created by a sentimental analysis of each tweet about the artist on the hashtag for a period of 2 to 4 months between the announcements of sales in the open sources and the direct sale of the work with the use of the two dictionaries AFINN and NRC.
Supporting agencies: The article is based on the results of the research carried out at the expense of government funds of the Financial University. Moscow, 2019.
About the Authors
Elena A. Fedorova
National Research University Higher School of Economics, Moscow
Russian Federation
Russian Federation
Diana V. Zaripova
National Research University Higher School of Economics, Moscow
Russian Federation
Russian Federation
Igor S. Demin
Financial University under the Government of the Russian Federation, Moscow
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Review
For citations:
Fedorova E.A., Zaripova D.V., Demin I.S. Factors of Price Formation for Art Objects With the Application of Text Analysis of Twitter News. Economics of Contemporary Russia. 2020;(2):114-131. (In Russ.) https://doi.org/10.33293/1609-1442-2020-2(89)-114-131
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