Forecasting US movies box office performances in Turkey using machine learning algorithms

dc.contributor.author Çağlıyor, Sendi
dc.contributor.author Oztaysi, Briar
dc.contributor.author Sezgin, Selime
dc.contributor.other Business Administration
dc.date.accessioned 2021-07-22T15:59:14Z
dc.date.available 2021-07-22T15:59:14Z
dc.date.issued 2020
dc.description.abstract The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.3233/JIFS-189120 en_US
dc.identifier.endpage 6590 en_US
dc.identifier.issn 1064-1246 en_US
dc.identifier.issn 1064-1246
dc.identifier.issue 5 en_US
dc.identifier.scopus 2-s2.0-85096978305 en_US
dc.identifier.scopusquality N/A
dc.identifier.startpage 6579 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/4087
dc.identifier.volume 39 en_US
dc.identifier.wos WOS:000595520600050 en_US
dc.identifier.wosquality N/A
dc.institutionauthor Cagliyora, Sandy en_US
dc.language.iso en en_US
dc.publisher IOS PRESS en_US
dc.relation.journal JOURNAL OF INTELLIGENT & FUZZY SYSTEMS en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 3
dc.subject Machine learning algorithms en_US
dc.subject motion picture industry en_US
dc.subject forecasting en_US
dc.title Forecasting US movies box office performances in Turkey using machine learning algorithms en_US
dc.type Article en_US
dc.wos.citedbyCount 1
dspace.entity.type Publication
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