Abstract
Over the past twenty years, people have seen considerable growth in film industry. There are two common measurements for movie quality, financial metric of net profit and reception metric in the form of ratings assigned by moviegoers on websites. Researchers have utilized these two metrics to build models for movie success prediction separately, while few of them investigate the combination. Therefore, in this paper, we analyze movie success from perspectives of financial and critical metrics in tandem. Here, optimal success is defined as a film that is both profitable and highly acclaimed, while its worst outcome involves financial loss and critical panning at the same time. Salient features that are salient to both financial and critical outcomes are identified in an attempt to uncover what makes a “good” movie “good” and a “bad” one “bad” as well as explain common phenomenons in movie industry quantitatively.
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Notes
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Box office mojo annual report: http://www.boxofficemojo.com/yearly/.
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References
Armstrong, N., Yoon, K.: Movie rating prediction. Technical report. Citeseer (1995)
Benesty, J., Chen, Y., Huang, Y., Cohen, I.: Pearson correlation coefficient. In: Noise Reduction in Speech Processing. Springer Topics in Signal Processing, vol. 2, pp. 1–4. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00296-0_5
Berg, J., Raddick, M.J.: First you get the money, then you get the reviews, then you get the internet comments: a quantitative examination of the relationship between critics, viewers, and box office success. Q. Rev. Film Video 34, 101–129 (2017)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Brown, A.L., Camerer, C.F., Lovallo, D.: To review or not to review? Limited strategic thinking at the movie box office. Am. Econ. J. Microecon. 4(2), 1–26 (2012)
Ding, C., Cheng, H.K., Duan, Y., Jin, Y.: The power of the “like” button: the impact of social media on box office. Decis. Support Syst. 94, 77–84 (2017)
Griffiths, T.: Gibbs sampling in the generative model of latent Dirichlet allocation (2002)
Karniouchina, E.V.: Impact of star and movie buzz on motion picture distribution and box office revenue. Int. J. Res. Mark. 28(1), 62–74 (2011)
Lash, M., Fu, S., Wang, S., Zhao, K.: Early prediction of movie success — what, who, and when. In: Agarwal, N., Xu, K., Osgood, N. (eds.) SBP 2015. LNCS, vol. 9021, pp. 345–349. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16268-3_41
Legoux, R., Larocque, D., Laporte, S., Belmati, S., Boquet, T.: The effect of critical reviews on exhibitors’ decisions: do reviews affect the survival of a movie on screen? Int. J. Res. Mark. 33(2), 357–374 (2016)
Lehrer, S., Xie, T.: Box office buzz: does social media data steal the show from model uncertainty when forecasting for hollywood? Technical report, National Bureau of Economic Research (2016)
Liu, T., Ding, X., Chen, Y., Chen, H., Guo, M.: Predicting movie box-office revenues by exploiting large-scale social media content. Multimed. Tools Appl. 75(3), 1509–1528 (2016)
Mestyán, M., Yasseri, T., Kertész, J.: Early prediction of movie box office success based on wikipedia activity big data. PloS One 8(8), e71226 (2013)
Moon, S., Bergey, P.K., Iacobucci, D.: Dynamic effects among movie ratings, movie revenues, and viewer satisfaction. J. Mark. 74(1), 108–121 (2010)
Oh, C., Roumani, Y., Nwankpa, J.K., Hu, H.-F.: Beyond likes and tweets: Consumer engagement behavior and movie box office in social media. Inf. Manag. (2016)
Pearson, K.: Liii on lines and planes of closest fit to systems of points in space. London, Edinburgh, Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901)
Ravid, S.A.: J. Bus. 72(4), 463–492 (1999)
Sharan, P.: Movie success predictor. Indian J. Appl. Res. 6(6) (2016)
Wang, H., Guo, K.: The impact of online reviews on exhibitor behaviour: evidence from movie industry. Enterp. Inf. Syst., 1–17 (2016)
Zhang, F., Yang, Y.: The effect of internet word-of-mouth on experience product sales—an empirical study based on film online reviews. Int. J. Bus. Adm. 7(2), 72 (2016)
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Gao, Z., Malic, V., Ma, S., Shih, P. (2019). How to Make a Successful Movie: Factor Analysis from both Financial and Critical Perspectives. In: Taylor, N., Christian-Lamb, C., Martin, M., Nardi, B. (eds) Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science(), vol 11420. Springer, Cham. https://doi.org/10.1007/978-3-030-15742-5_63
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