A Machine Learning Approach to Predict Movie Revenue Based on Pre-Released Movie Metadata

Mahmud, Quazi Ishtiaque; Shuchi, Nuren Zabin; Tawsif, Fazle Mohammed; Mohaimen, Asif; Tasnim, Ayesha

doi:10.3844/jcssp.2020.749.767

Cited by 3 publications

(3 citation statements)

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“…С развитием киноиндустрии все более важным становится прогнозирование прибыли, которую получит фильм в прокате, особенно с учетом культурных различий на домашнем и зарубежных рынках (Gao et al, 2020). Фактически, среди фильмов, произведенных в США в период с 2000 по 2010 г., кассовые сборы только 36% из них превысили производственные бюджеты, что еще раз подчеркивает важность принятия правильных инвестиционных решений продюсерами (Mahmud et al, 2020).…”

Section: теоретические подходы к проблемам потребительского выбора на...unclassified

“…Более новые модели используют 35 параметров фильма из 3200 фильмов в качестве входных данных для прогнозирования прибыли, полученной от фильма, и классификации успеха фильма от «провала» до «блокбастера» на основе полученной прибыли (An et al, 2021;Bogaert et al, 2021). В исследованиях также представлен анализ тех или иных архитектур машинного обучения, способных предсказать прибыль, полученную в результате проката фильма, с точностью до 85,31% без использования какой-либо информации о продажах (Abidi et al, 2020;Mahmud et al, 2020).…”

Section: теоретические подходы к проблемам потребительского выбора на...unclassified

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Main consumer choice drivers for fi lm products in the Russian market

Gerasimenko,

Kheirbeik

2024

LEJ

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Film distribution market is a highly competitive one, which only emphasizes the need to optimize costs for the production and promotion of domestic films while increasing box office potential. The purpose of this research is to identify the primary choice drivers for movies in the movie theater in current market conditions as a required prerequisite for effective promotion. The results of a content analysis of open data tracker Cinema Viewer in 2021 allow us to identify several drivers defining the choice of moviegoers. They can be divided into two main categories: content elements and promotional elements. Among content elements, the most important are film's genre, plot, and cast. The most important promotional element is the trailer. Furthermore, two findings stand out among the results of this study, which require detailed research in the further works on the subject, as they can significantly influence the promotional strategies for film products in the Russian market. Firstly, the study showed that information itself, rather than its source, is more important for the viewer, which means that further optimization of marketing budgets is possible by reducing spending on most expensive platforms (primarily TV). Secondly, recommendations play just as important a role in the choice of film as advertising, which also makes it possible to optimize the marketing budget through social media marketing.

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Section: теоретические подходы к проблемам потребительского выбора на...unclassified

Main consumer choice drivers for fi lm products in the Russian market

Gerasimenko,

Kheirbeik

2024

LEJ

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“…e results show that the system is effective for a film recommendation. In order to solve the problems existing in the process of film investment, a model is established using different machine learning algorithms [6]. e model can predict the box office return of the film according to the data before the release of the film so as to propose a certain investment direction for investors.…”

Section: Introductionmentioning

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Correlation Analysis of Network Big Data and Film Time-Series Data Based on Machine Learning Algorithm

Li¹,

Xia

2022

Mathematical Problems in Engineering

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To expand the application of machine learning in movie data, in order to explore the correlation between network big data and film time-series data, based on the machine learning algorithm, the correlation and multifractal characteristics of happiness index (HI) and film box office (BO) were studied and described by introducing multifractal crossover method. On this basis, some indicators are introduced to optimize the neural network model so that the optimization model can describe and predict the box office and other related information well. The results show that the critical values of the happiness index and box office show a linear change trend with the increase of freedom, and the corresponding change curves of the happiness index and box office show obvious nonlinear characteristics, which can be divided into slow increase stage, steady increase stage, and approximately gentle stage. With the increase of iteration parameter q value, the change trend of the long-term and short-term curves of the generalized Hurst function is basically the same, and the difference between the two is getting smaller and smaller, while the difference between the two curves is getting bigger and bigger with the increase of q value of Renyi function. The changing trend of the dynamic Hurst index in the sliding window period all shows that it first rises rapidly to a certain value, then fluctuates rapidly with the increase of time, then drops rapidly to a constant value, and finally continues to show repeated small range fluctuation. Under the influence of time-series parameter α, the original sequence changes the most, the replacement sequence changes the medium, and the corresponding rearrangement sequence changes the least. The overall distribution of box office prediction data conforms to the characteristics of linear variation. The prediction index of the optimized HI-LSTM (Happiness Index-Long term short term memory neural network) model is higher in the box office, indicating that the model has better performance in describing and predicting the box office. This study can provide a theoretical basis for the correlation study of network big data and film data.

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Presenting a Larger Up-to-Date Movie Dataset and Investigating the Effects of Pre-Released Attributes on Gross Revenue

Sharma¹,

Roy²,

Rifat³

et al. 2021

Journal of Computer Science

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Movie-making has become one of the most costly and risky endeavors in the entertainment industry. Continuous change in the preference of the audience makes it harder to predict what kind of movie will be financially successful at the box office. So, it is no wonder that cautious, intelligent stakeholders and large production houses will always want to know the probable revenue that will be generated by a movie before making an investment. Researchers have been working on finding an optimal strategy to help investors in making the right decisions. But the lack of a large, up-to-date dataset makes their work harder. In this study, we introduce an up-to-date, richer and larger dataset that we have prepared by scraping IMDb for researchers and data analysts to work with. The compiled dataset contains the summery data of 7.5 million titles and detail information of more than 200K movies. Additionally, we perform different statistical analysis approaches on our dataset to find out how a movie's revenue is affected by different pre-released attributes such as budget, runtime, release month, content rating, genre etc. In our analysis we have found that having a star cast/director has a positive impact on generated revenue. We introduce a novel approach for calculating the star power of a movie. Based on our analysis we select a set of attributes as features and train different machine learning algorithms to predict a movie's expected revenue. Based on generated revenue, we classified the movies in 10 categories and achieved a one-class-away accuracy rate of almost 60% (bingo accuracy of 30%). All the generated datasets and analysis codes are available online. We also made the source codes of our scraper bots public, so that researchers interested in extending this study can easily modify these bots as they need and prepare their own up-todate datasets.

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A Machine Learning Approach to Predict Movie Revenue Based on Pre-Released Movie Metadata

Cited by 3 publications

References 31 publications

Main consumer choice drivers for fi lm products in the Russian market

Main consumer choice drivers for fi lm products in the Russian market

Correlation Analysis of Network Big Data and Film Time-Series Data Based on Machine Learning Algorithm

Presenting a Larger Up-to-Date Movie Dataset and Investigating the Effects of Pre-Released Attributes on Gross Revenue

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