Publicação
Forecasting Movie Box Office Profitability
| dc.contributor.author | Galvão, Marta | |
| dc.contributor.author | Henriques, Roberto | |
| dc.contributor.institution | NOVA Information Management School (NOVA IMS) | |
| dc.contributor.institution | Information Management Research Center (MagIC) - NOVA Information Management School | |
| dc.contributor.pbl | IADITI - International Association for Digital Transformation and Technological Innovation | |
| dc.date.accessioned | 2018-07-30T22:09:37Z | |
| dc.date.available | 2018-07-30T22:09:37Z | |
| dc.date.issued | 2018-07-16 | |
| dc.description | Galvão, M., & Henriques, R. (2018). Forecasting Movie Box Office Profitability. Journal of Information Systems Engineering & Management, 3(3), 1-9. [22]. DOI: 10.20897/jisem/2658 | |
| dc.description.abstract | This study intends to estimate the profit of a movie through the construction of a predictive model that uses several Data Mining techniques, namely neural networks, regression and decision trees. The model will allow obtaining the prediction of box office revenue. Three different dependent variable approaches were used (interval, categorical and binary) aiming to study the difference and predictive influence that each one has on the results. Two metrics were used to determine the most accurate predictions: the misclassification error for the categorical models and the average squared error for the continuous one. In this study, the best predictive results were obtained through the use of multi-layer perceptron. Regarding the representative distinction between the dependent variable, the multiclass model presents a much higher error rate comparing to the remaining ones, which is explained with the increase of the number of classes to predict. | en |
| dc.description.version | published | |
| dc.format.extent | 9 | |
| dc.format.extent | 299138 | |
| dc.identifier.doi | 10.20897/jisem/2658 | |
| dc.identifier.issn | 2468-4376 | |
| dc.identifier.other | PURE: 5631705 | |
| dc.identifier.other | PURE UUID: 139076b9-1371-4251-9b8d-f363d2256b7e | |
| dc.identifier.other | crossref: 10.20897/jisem/2658 | |
| dc.identifier.other | ORCID: /0000-0002-4862-8177/work/152174831 | |
| dc.identifier.uri | http://www.lectitopublishing.nl/Article/Detail/forecasting-movie-box-office-profitability | |
| dc.identifier.url | http://www.lectitopublishing.nl/Article/Detail/forecasting-movie-box-office-profitability | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.subject | Data mining | |
| dc.subject | Movie profitability | |
| dc.subject | Box office profit | |
| dc.subject | Predictive analysis | |
| dc.subject | Neural networks | |
| dc.subject | Decision tree | |
| dc.subject | Regression | |
| dc.title | Forecasting Movie Box Office Profitability | en |
| dc.type | journal article | |
| degois.publication.firstPage | 1 | |
| degois.publication.issue | 3 | |
| degois.publication.lastPage | 9 | |
| degois.publication.title | Journal of Information Systems Engineering & Management | |
| degois.publication.volume | 3 | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess |
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