Predicting Box Office from the Screenplay: A Text Analytical Approach

@article{Hunter2016PredictingBO,
  title={Predicting Box Office from the Screenplay: A Text Analytical Approach},
  author={Starling David Hunter and Susan Smith and Saba Singh},
  journal={Film eJournal - Forthcoming},
  year={2016}
}
Empirical studies of the determinants of box office revenues have mostly focused on post-production factors – that is, ones known after the film has been completed and/or released. Relatively few studies have considered pre-production factors – that is, ones known before a decision has been made to green-light a film project. The current study directly addresses this gap in the literature. Specifically, we develop and test a relatively parsimonious, pre-production model to predict the opening… 
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References

SHOWING 1-10 OF 84 REFERENCES
Information, Blockbusters and Stars? A Study of the Film Industry
The purpose of this paper is to explore the role of stars and other potential informational signals in the movie business. In the first part of the paper, we explore two alternative economic
A Network Text Analysis of Fight Club
TLDR
This paper first establishes prior expectations as to the key themes to be found in the text, then compares and contrast the results of the network analysis with theresults of literary and cultural analyses of the film Fight Club as reported in over four dozen other peer-reviewed publications.
The pricing of soft and hard information: economic lessons from screenplay sales
This paper uses a unique data set on screenplay sales to learn how the information content of a sales pitch affects sale prices. This is one of the few studies that analyze “soft information” outside
A Semi-Automated Method of Network Text Analysis Applied to 150 Original Screenplays
TLDR
A novel method of network text analysis is applied to a sample of 150 original screenplays and it is found that the text networks derived from unproduced screenplays are significantly less complex and more cohesive, i.e. they exhibit higher density and coreness.
Predicting box-office success of motion pictures with neural networks
THE DETERMINANTS OF BOX OFFICE REVENUE FOR DOCUMENTARY MOVIES
This paper examines the domestic box office revenue determinants of movies from the documentary genre. The sample consists of the top 100 gross box office revenue documentary films released during
A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures
The primary objective of this paper is to develop a parsimonious model for forecasting the gross box-office revenues of new motion pictures based on early box office data. The paper also seeks to
Assessing Box Office Performance Using Movie Scripts: A Kernel-Based Approach
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
A Novel Method of Network Text Analysis
TLDR
It is shown that statements associated with a text network’s least constrained nodes are consistent with themes in the films’ synopses found on Wikipedia, the International Movie Database, and Rotten Tomatoes.
Predicting box office with and without markets: Do internet users know anything?
...
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