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What can Hollywood Learn from YouTube?

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dc.contributor.author Lewis, John Jack
dc.date.accessioned 2021-05-07T00:32:57Z
dc.date.available 2021-05-07T00:32:57Z
dc.date.issued 2021-05
dc.identifier.uri http://hdl.handle.net/11416/554
dc.description Honors Thesis Spring 2021 en_US
dc.description.abstract Though most of a film’s marketing budget is directed toward television advertisements, the rise in video-sharing platforms provides an additional outlet for promotion. Arguably, the most notable of these video-sharing platforms is YouTube. This paper addresses how the data of a film trailer released on YouTube – like number of views, comments, likes, and dislikes – help predict the generated revenue for a film, additionally exploring how these features expand on the effect of other features in the filmmaking process. The data used to evaluate these features is generated from films released in the 2010s in The Movie Database (TMDb) and video trailer statistics from the YouTube Data API. Specifically, these features are used to train machine learning models to evaluate which models are most successful in revealing connections between these features and revenue. This work closely examines the impact of the statistics of a film trailer from YouTube to help producers better understand the impact of their investments. en_US
dc.publisher Florida Southern College en_US
dc.subject Motion pictures en_US
dc.subject Machine learning en_US
dc.subject Decision trees en_US
dc.subject Neural networks (Computer science) en_US
dc.subject Regression analysis en_US
dc.subject Motion picture industry en_US
dc.subject Motion pictures -- Reviews en_US
dc.subject YouTube en_US
dc.title What can Hollywood Learn from YouTube? en_US
dc.type Thesis en_US


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