Abstract
In this research work, movie box-office gross revenue estimation has been performed using machine learning techniques to effectively estimate the amount of gross revenue a movie will be able to collect using the public information available after its first weekend of release. Here, first weekend refers to first three days of release namely Friday, Saturday, and Sunday. This research work has been done only for the movies released in USA. It was assumed that gross revenue is equal to the amount of money that is collected by the sale of movie tickets. Data collected has been collected from IMDB and Rotten Tomatoes for movies released from the year 2000–2015 only. Multiple linear regression and genre-based analysis was used to effectively estimate the gross revenue. Finally, Local regression methods namely local linear regression, and local decision tree regression were used to get a better estimate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Eliashberg, J., Shugan, S.M.: Film critics: influencers or predictors? J. Mark. 68–78 (1997)
Ravid, S.A.: Information, blockbusters, and stars: a study of the film industry. J. Bus. 72(4), 463–492 (1999)
Reinstein, D.A., Snyder, C.M.: The influence of expert reviews on consumer demand for experience goods: a case study of movie critics. Working Paper, University of California-Berkeley and George Washington University (2000)
Litman, B.R.: Predicting success of theatrical movies: an empirical study. J. Pop. Cult. 16(4), 159–175 (1983)
Apte, N., Forssell, M., Sidhwa, A.: Predicting Movie Revenue. CS229, Stanford University (2011)
Yoo, S., Kanter, R., Cummings, D.: Predicting Movie Revenue from IMDb Data. Stanford University (2011)
Simonoff, J.S., Sparrow, I.R.: Predicting movie grosses: winners and losers, blockbusters and sleepers. Chance 13(3), 15–24 (2000)
Mestyn, M., Yasseri, T., Kertsz, J.: Early prediction of movie box office success based on Wikipedia activity big data. PloS One 8(8), e71226 (2013)
Prasad, B.R., Agarwal, S.: Comparative study of big data computing and storage tools: a review. Int. J. Database Theory Appl. 9(1), 45–66 (2016)
Prasad, B.R., Agarwal, S.: Stream data mining: platforms, algorithms, performance evaluators and research trends. Int. J. Database Theory Appl. 9(9), 201–218 (2016)
Predicting Box Office Revenue for Movies: Matt Vitelli (2015)
Sharda, R., Delen, D.: Predicting box-office success of motion pictures with neural networks. Expert Syst. Appl. 30(2), 243–254 (2006)
Anast, P.: Differential movie appeals as correlates of attendance. J. Q. 44(1), 86–90 (1967)
de Silva, B., Compton, R.: Prediction of foreign box office revenues based on wikipedia page activity (2014). arXiv:1405.5924
Hennig-Thurau, T., Houston, M.B., Walsh, G.: Determinants of motion picture box office and profitability: an interrelationship approach. Rev. Manag. Sci. 1(1), 65–92 (2007)
Internet Movie DataBase: http://www.imdb.com/
Rotten Tomatoes: https://www.rottentomatoes.com/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sachdev, S., Agrawal, A., Bhendarkar, S., Prasad, B.R., Agarwal, S. (2018). Movie Box-Office Gross Revenue Estimation. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-8633-5_2
Download citation
DOI: https://doi.org/10.1007/978-981-10-8633-5_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8632-8
Online ISBN: 978-981-10-8633-5
eBook Packages: EngineeringEngineering (R0)