Early Predictions of Movie Success: The Who, What, and When of Profitability

Abstract

We focus on predicting the profitability of a movie to support movie-investment decisions at early stages of film production. By leveraging data from various sources, and using social network analysis and text mining techniques, the proposed system extracts several types of features, including “who” is in the cast, “what” a movie is about, “when” a movie will be released, as well as “hybrid” features. Experiment results showed that the system outperforms benchmark methods by a large margin. Novel features we proposed made weighty contributions to the prediction. In addition to designing a decision support system with practical utility, we also analyzed key factors of movie profitability. Furthermore, we demonstrated the prescriptive value of our system by illustrating how it can be used to recommend a set of profit-maximizing cast members. This research highlights the power of predictive and prescriptive data analytics in information systems to aid business decisions.

Key words and phrases:

• decision support
• movie investments
• movie profitability
• predictive analytics
• prescriptive analytics
• social network analysis
• text mining

Additional information

Notes on contributors

Michael T. Lash

Michael T. Lash ([email protected]) is a Ph.D. student in the Department of Computer Science at University of Iowa. His research interests lie in the areas of data mining, machine learning, and predictive analytics. Specific interests as well as ongoing areas of research include inverse classification, utility-based data mining, adversarial learning, and survival analytics and learning. Application of these areas to health care, business, and entertainment domains are also of interest.

Kang Zhao

Kang Zhao ([email protected]; corresponding author) is an assistant professor at Tippie College of Business, University of Iowa. He is also affiliated with the university’s Interdisciplinary Graduate Program in Informatics. He obtained his Ph.D. from Penn State University. His research focuses on data science and social computing, especially in the contexts of social/business networks and social media. His research has been covered by the BBC, Washington Post, Forbes, and others in more than twenty countries.