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

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dc.contributor.authorCagliyora, Sandy
dc.contributor.authorOztaysi, Briar
dc.contributor.authorSezgin, Selime
dc.date.accessioned2024-07-18T20:49:19Z
dc.date.available2024-07-18T20:49:19Z
dc.date.issued2020
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractThe 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.doi10.3233/JIFS-189120
dc.identifier.endpage6590en_US
dc.identifier.issn1064-1246
dc.identifier.issn1875-8967
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85096978305en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage6579en_US
dc.identifier.urihttps://doi.org/10.3233/JIFS-189120
dc.identifier.urihttps://hdl.handle.net/11411/8169
dc.identifier.volume39en_US
dc.identifier.wosWOS:000595520600050en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIos Pressen_US
dc.relation.ispartofJournal of Intelligent & Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learning Algorithmsen_US
dc.subjectMotion Picture İndustryen_US
dc.subjectForecastingen_US
dc.subjectMotion-Picture Industryen_US
dc.subjectWord-Of-Mouthen_US
dc.subjectRevenuesen_US
dc.subjectDynamicsen_US
dc.subjectReviewsen_US
dc.subjectDeterminantsen_US
dc.subjectHollywooden_US
dc.subjectSuccessen_US
dc.subjectMarketsen_US
dc.subjectImpacten_US
dc.titleForecasting US movies box office performances in Turkey using machine learning algorithmsen_US
dc.typeArticleen_US

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