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". Our experiments with movies during an 11-year period showcase the system's decent performance in predicting movie success. Moreover, experiment results also suggest that different groups of features, including novel features we proposed, all contribute to the prediction.
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