Predictive model for movie’s success and sentiment analysis
- 1School of Business, Alliance University, 19th Cross, 7th Main, BTM 2nd Stage, N.S. Palya, Bengaluru – 560 076, India
- 2Marketing & Business Intelligence, TVS next Pvt. Ltd., ASV Chandilya Towers, OMR, Chennai-600096, India
Res. J. Management Sci., Volume 6, Issue (6), Pages 1-7, June,6 (2017)
The film industry is one of the biggest contributors to the entertainment industry and also it is characterized with its unpredictability in success and Failure. Film Industry has always amused everyone with its unpredictable success and Failure. The Indian scenario works a lot different than the western movies; a lot of importance is normally given to different parameters such as celebrity appeal, the movie album and others, which is an integral part of the movie itself; unlike, the western movies. This research looks into the inner details of watching a movie by splitting the research into three main components. First section is exploring the variables that influence the frequency of movie watch; second, developing a model to predict the success or failure. Finally, social network sentiment analysis is carried out through data mining to capture the audience sentiment and its impact on movie’s success and failure. The research tries to look at the success or failure of a movie on a more holistic manner than trying to grade the performance of a movie over a few variables based on the previous research works on movie success prediction.
- Litman B.R. and Kohl Linda S. (1998)., Predicting Financial Success of Motion Pictures., Journal of Media Economics, 2(2), 35-50.
- Litman B.R. (1983)., Predicting success of theatrical movies: An empirical study., The Journal of Popular Culture, 16(4), 159-175.
- South-Africa, Nigeria and Kenya (2015)., Entertainment and media outlook: 2015-2019., , https://www.pwc.co. za/en/ assets/pdf/entertainment-and-media-outlook-2015-2019.pdf, Accessed on April 2017.
- Indian films, Business Standard., http://www.business-standard.com/article/pti-stories/indian-films-box-office-collection-to-be-usd-3-7-bn-in-2020-116092500337_1.html, Accessed on April 2017
- Deloitte (2014)., Digitization & Mobility: Next Frontier of Growth for M&E 2016., https://www2.deloitte.com /content/dam/Deloitte/in/Documents/technology-media-telecommunications/in-tmt-digitization-n-mobility-noexp.pdf , Accessed on March 2017.
- Top 10 Film Countries by Box Office (2013). http://www.filmcontact.com/americas/united-states/top-10-film-countries-box-office, Accessed on April 2017, undefined, undefined
- Elberse A. and Eliashberg J. (2002)., The drivers of motion picture performance: the need to consider dynamics, endogeneity and simultaneity., proceedings of the Business and Economic Scholars Workshop in Motion picture Industry Studies. Florida Atlantic University, 1-15.
- Neelamegham R. and Chintagunta P. (1999)., A Bayesian model to forecast new product performance in domestic and international markets., Marketing Science, 18(2), 115-136.
- Krauss J., Nann S., Simon D., Gloor P.A. and Fischbach K. (2008)., Predicting Movie Success and Academy Awards through Sentiment and Social Network Analysis., ECIS, 2026-2037.
- Simonoff J.S. and Sparrow I.R. (2000)., Predicting movie grosses: Winners and losers, blockbusters and sleepers., Chance, 13(3), 15-24.
- Zhang W. and Skiena S. (2009)., Improving movie gross prediction through news analysis., Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology-Volume 01, 301-304. IEEE Computer Society.
- Mestyán M., Yasseri T. and Kertész J. (2013)., Early prediction of movie box office success based on Wikipedia activity big data., PloS one, 8(8), 12-26.
- Sharda R. and Delen D. (2006)., Predicting box-office success of motion pictures with neural networks., Expert Systems with Applications, 30(2), 243-254.
- Deniz B. and Hasbrouck Robert B. (2012)., What Determines Box Office Success of A Movie in the United States., Chistoper Newport University, 1-11.
- Prag J. and Casavant J. (1994)., An empirical study of the determinants of revenues and marketing expenditures in the motion picture industry., Journal of Cultural Economics, 18(3), 217-235.
- King Timothy (2007)., Does film criticism affect box office earnings? Evidence from moviesreleased in the U.S. in 2003., Journal of Cultural Economics, 31(3), 171-186.
- Ravid S.A. (1999)., Information, blockbusters, and stars: a study of the film industry., The Journal of Business, 72(4), 463-492.
- Joshi M., Das D., Gimpel K. and Smith N.A. (2010)., Movie reviews and revenues: An experiment in text regression., Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, 293-296.
- Mishne G. and Glance N.S. (2006)., Predicting Movie Sales from Blogger Sentiment., AAAI spring symposium: computational approaches to analyzing weblogs, 155-158.
- Zhang W. and Skiena S. (2009)., Improving movie gross prediction through news analysis., Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology-Volume 01. IEEE Computer Society, 301-304.