Abstract
Computational prediction of a movie's financial success usually relies only on metadata such as — genre, budget, actors, Motion Picture Association of America (MPAA) rating and critics' reviews. We argue that movie trailers, created to invoke viewers' interest and curiosity about a movie, carry complementary information for predicting a movie's financial future. We created a database consisting of 474 American movie trailers along with various metadata and information about movie's financial success in the opening weekend. A number of features that capture the emotional information contained in the audiovisual stream of the trailers are designed and extracted. We observe that the content-based features have as much predictive information as the meta features. Through regression analysis on our database, we show that signal information from trailer content improves the prediction performance.
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