ecr-journalЭкономическая наука современной РоссииEconomics of Contemporary Russia1609-14422618-8996Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences10.33293/1609-1442-2020-2(89)-114-131ecr-journal-466Research ArticleИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ В ЭКОНОМИКЕINFORMATIONAL TECHNOLOGIES IN ECONOMICSФакторы формирования цены на предметы искусства с применением текстового анализа новостей в ТвиттереFactors of Price Formation for Art Objects With the Application of Text Analysis of Twitter Newshttps://orcid.org/0000-0002-3381-6116ФедороваЕлена АнатольевнаFedorovaElena A.

профессор ФУ и НИУ ВШЭ

ecolena@mail.ruЗариповаДиана ВилевнаZaripovaDiana V.ecolena@mail.ruДеминИгорь СергеевичDeminIgor S.ecolena@mail.ruФинансовый университет при Правительстве РФ, Москва; Национальный исследовательский университет Высшая школа экономики, МоскваРоссияNational Research University Higher School of Economics, MoscowRussian FederationНациональный исследовательский университет Высшая школа экономики, МоскваРоссияNational Research University Higher School of Economics, MoscowRussian FederationФинансовый университет при Правительстве РФ, МоскваRussian FederationFinancial University under the Government of the Russian Federation, MoscowRussian Federation20200604202002114131Copyright © Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, 20202020Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of SciencesRegional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Scienceshttps://www.ecr-journal.ru/jour/about/submissions#copyrightNoticehttps://www.ecr-journal.ru/jour/article/view/466

В данной работе были подтверждены гипотезы о влиянии индекса настроений в сети Твиттер на ценообразование предметов искусства и разницу между предварительной оценкой экспертов и итоговой ценой аукциона. Гипотезы были протестированы с помощью выборки из 83 картин, выбранных на основе рейтингов интернет-ресурса ARTNET o самых дорогих когда-либо проданных произведениях искусства за последние 10–15 лет. Выборка состояла из 25 художников, по каждому из них был составлен индекс настроений в сети Твиттер. Данный индекс был создан путем проведения сентимент-анализа каждого твита о художнике по хэштегу за период от двух до четырех месяцев между анонсами продажи в открытых источниках и непосредственной продажей работы по двум словарям AFINN и NRC.

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.

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The authors declare that there are no conflicts of interest present.