Early Predictions of Movie Success: the Who, What, and When of Profitability
It addresses the practical problem of aiding movie investment decisions for producers and investors, with incremental improvements through novel feature extraction.
This paper tackles the problem of predicting movie profitability early in production by developing a decision support system that uses social network analysis and text mining to extract features from cast, content, and release timing, outperforming benchmark methods by a large margin.
This paper proposes a decision support system to aid movie investment decisions at the early stage of movie productions. The system predicts the success of a movie based on its profitability by leveraging historical data from various sources. Using social network analysis and text mining techniques, the system automatically extracts several groups of features, including "who" are on the cast, "what" a movie is about, "when" a movie will be released, as well as "hybrid" features that match "who" with "what", and "when" with "what". Experiment results with movies during an 11-year period showed that the system outperforms benchmark methods by a large margin in predicting movie profitability. Novel features we proposed also made great contributions to the prediction. In addition to designing a decision support system with practical utilities, our analysis of key factors for movie profitability may also have implications for theoretical research on team performance and the success of creative work.