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Assessing Box Office Performance Using Movie Scripts: A Kernel-Based Approach
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DOI：10.1109/TKDE.2014.2306681.png)
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Abstract
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We develop a methodology to predict box office performance of a movie at the point of green-lighting, when only its script and estimated production budget are available. We extract three levels of textual features (genre and content, semantics, and bag-of-words) from scripts using screenwriting domain knowledge, human input, and natural language processing techniques. These textual variables define a distance metric across scripts, which is then used as an input for a kernel-based approach to assess box office performance. We show that our proposed methodology predicts box office revenues more accurately (29 percent lower mean squared error (MSE)) compared to benchmark methods.
Keywords:
Entertainment industry
green-lighting
movie production
text mining
kernel approach
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