An Optimization Focused Machine Learning Approach in Analysing Arts Participative Behavior with Fine Arts Education Considerations

Year 2021, Volume: 5 Issue: 2, 241 - 253, 31.12.2021

Özerk Yavuz

https://doi.org/10.47897/bilmes.1029139

Cited By: 2

Abstract

Fine arts education constitutes an important value for the development of nations, countries and geographies. In literature it is seen that societies and nations that give importance to fine arts, other art forms with respective education programs and agendas seem to be more developed in the fierce competitive environments. It is a known fact that art forms enhance the creativity of individuals as well as developing questioning skills and empathy. Therefore cultural plans, programs and agendas handled in the cultural ministry or similar levels should always be considered as a chance for investments made for the development of individuals and society at large. Therefore enough budgets for cultural programs should be maintained. Academic and pre-university degree art programs should be designed and formed in response to the changes and developments in the contemporary art world. Graduates should also be prepared in these programs to the career paths that they want to follow in these areas. Understanding the domain of art marketing as well as implementing marketing strategies, plans, programs in every level in this context can be helpful in enlarging the arts market which means creating more jobs and employment opportunities for more actors and actresses. Qualification of these talents should also be supported with academical or nonacademical artistic education forms. In this study theatres located in Istanbul
Municipality and theatre participation behavior has been analyzed with recent machine learning trends and approaches available in data mining literature with the aim of enhancing exploratory and confirmatory understanding in such domains as theater, entertainment and art marketing with the aim of providing some insights and ideas to the arts community, leaders and society at large. 

Keywords

Fine Arts, Fine Arts Marketing, Fine Arts Education, Entertainment, Entertainment Marketing, Theater Marketing, Art Marketing, Clustering, Classification, Data Mining, Machine Learning, Quantitative Analysis, Supervised Learning, Unsupervised Learning

An Optimization Focused Machine Learning Approach in Analysing Arts Participative Behavior with Fine Arts Education Considerations

Abstract

Fine arts education constitutes an important value for the development of nations, countries and geographies. In literature it is seen that societies and nations that give importance to fine arts, other art forms with respective education programs and agendas seem to be more developed in the fierce competitive environments. It is a known fact that art forms enhance the creativity of individuals as well as developing questioning skills and empathy. Therefore cultural plans, programs and agendas handled in the cultural ministry or similar levels should always be considered as a chance for investments made for the development of individuals and society at large. Therefore enough budgets for cultural programs should be maintained. Academic and pre-university degree art programs should be designed and formed in response to the changes and developments in the contemporary art world. Graduates should also be prepared in these programs to the career paths that they want to follow in these areas. Understanding the domain of art marketing as well as implementing marketing strategies, plans, programs in every level in this context can be helpful in enlarging the arts market which means creating more jobs and employment opportunities for more actors and actresses. Qualification of these talents should also be supported with academical or nonacademical artistic education forms. In this study theatres located in Istanbul Municipality and theatre participation behavior has been analyzed with recent machine learning trends and approaches available in data mining literature with the aim of enhancing exploratory and confirmatory understanding in such domains as theater, entertainment and art marketing with the aim of providing some insights and ideas to the arts community, leaders and society at large. 

Keywords

Fine Arts, Fine Arts Marketing, Fine Arts Education, Entertainment, Entertainment Marketing, Theater Marketing, Art Marketing, Clustering, Classification, Data Mining, Machine Learning, Quantitative Analysis, Supervised Learning, Unsupervised Learning

References

• [1] https://library.harvard.edu/collections/harvard-theatre-collection
• [2] https://plato.stanford.edu/entries/theater/
• [3] https://www.gsb.stanford.edu/insights/why-does-movie-popcorn-cost-so-much
• [4] https://arts.stanford.edu/directory/theater-performance/
• [5] https://en.wikipedia.org/wiki/Theatres_Act_1968
• [6] https://en.wikipedia.org/wiki/History_of_theatre
• [7] https://www.timeout.com/newyork/theater/best-plays-of-all-time
• [8] Goldstein R.J. (1989) Political Censorship of the Theatre. In: Political Censorship of the Arts and the Press in Nineteenth-Century Europe. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-20128-0_4
• [9] Tamarchenko A. Theatre Censorship. Index on Censorship. 1980;9(4):23-28.
• [10] Moise, Daniel & Bogdan, Georgescu & Daniel, Zgură. (2012). The Use of Event Marketing Management Strategies. Procedia - Social and Behavioral Sciences. 46. 10.1016/j.sbspro.2012.06.448.