Prevision model and empirical test of box office results for sequels

Belvaux, Bertrand; Mencarelli, Rémi

doi:10.1016/j.jbusres.2021.03.008

Cited by 7 publications

(2 citation statements)

References 45 publications

(121 reference statements)

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“…When developing box office forecasts, it is critical to take sequels into account because sequels have a unique place in the motion picture business since they are connected with the original film. Studies have shown that reviews and ratings of the original film have a direct impact on the performance of the sequel [20], and many sequels have a marketing advantage with a reputation built up by the film's predecessor. Special Effect.…”

Section: Feature Descriptionmentioning

confidence: 99%

A Deep Learning Approach to Predict Film Box Office in the Chinese Domestic Market Based on Feed-Forward Neural Network

Lan¹,

He²

2023

Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19–21, 2023, Han

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With the booming development of the Chinese film market, China has become the world's second-largest film market and the main engine for industry development. In order to improve the efficiency and predictive power of movie box office prediction in the Chinese domestic market, this study proposes a Feed-Forward Neural Networks model with two and three hidden layers by predicting the box office of 478 films in the Chinese market from 2019 to 2022 using movie metadata, post-release rating data, social media related big data and theaters arrangement. Loss curves for the training and validation sets are presented for model comparison with 400 films being used as the training set and 78 films being used as the validation set. The results show that the Feed-Forward Neural Network with three hidden layers has greater fitting and predictive power in model generation, enabling it to effectively predict the movie box office.

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Section: Feature Descriptionmentioning

confidence: 99%

A Deep Learning Approach to Predict Film Box Office in the Chinese Domestic Market Based on Feed-Forward Neural Network

Lan¹,

He²

2023

Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19–21, 2023, Han

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“…Among them, the three major factors of celebrity influence, comment, and word of mouth have received extensive attention [9]. In addition, in view of the huge economic impact of film sequel products in the film industry, scholars have begun to study the impact of this factor [10]. With the explosive growth and development of digital technology, movie consumers can express their opinions or attitudes towards products across space and time.…”

Section: Introductionmentioning

confidence: 99%

Research on the Influencing Factors of Film Consumption and Box Office Forecast in the Digital Era: Based on the Perspective of Machine Learning and Model Integration

2021

Wireless Communications and Mobile Computing

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The film industry is one of the core industries of the digital creative industry, which has great positive externalities to the digital creative economy. Movie box office revenue is an important indicator to measure the realization of the market value of movie consumption, and it is also the basic guarantee for the sustainable development of the movie industry. This paper relies on the professional database of the Maoyan movie market to use Python software to collect a total of 830 domestic movie-related consumption characteristic data from 2017 to 2019. In this study, the stacking method in the machine learning ensemble algorithm combines the fivefold crossfolding training method based on distributed random forest, extremely randomized trees, and generalized linear models. The model is good at handling different data types. It has higher fitting and model accuracy in feature mining and model construction, so as to effectively grasp the relevant feature factors affecting movie consumption and accurately predict the future movie box office. Based on the innovative design method of model fusion, the extracted feature vector is used to build a more accurate movie box office prediction model through stacking with a fivefold crossfolding training method. It is aimed at opening the black box that affects the realization of the value of the film content consumption market in the digital age and putting forward corresponding countermeasures and suggestions.

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