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Artificial Neural Network Decides Movie Success Even Before Shooting: Cinema Dies

Will the 3-hour special-effects-loaded remake of King Kong be a box office smash or a complete turkey? For movie producers, getting such questions right can be worth millions, and now they have a computer system to help them work it out before a film is even made.

The idea comes from Ramesh Sharda, an information scientist at Oklahoma State University in Stillwater, who has trained an artificial neural network to recognise what makes a successful movie.

Using data on 834 movies released between 1998 and 2002, Sharda found that the neural network can judge a film based on seven key parameters: the “star value” of the cast, the movie’s age rating, the time of release against that of competitive movies, the film’s genre, the degree of special effects used, whether it is a sequel or not, and the number of screens it is expected to open in. This allowed it to place a movie in one of nine categories, ranging from “flop” (total takings less than $1 million) to “blockbuster” (over $200 million).

The system cannot take into account the intricacies of the plot, but Sharda says it can nonetheless get the revenue category spot-on 37 per cent of the time, and correct to within one category either side 75 per cent of the time. This is enough to make the system a “powerful decision aid”, Sharda says…

Published in researchmaterial