Artificial intelligence predicts which movies will succeed—and fail—simply from plot summaries

Artificial intelligence predicts which movies will succeed—and fail—simply from plot summaries

5 years ago
Anonymous $9jpehmcKty

http://www.sciencemag.org/news/2019/08/artificial-intelligence-predicts-which-movies-will-succeed-and-fail-simply-plot

Artificial intelligence (AI) still can’t see the future, but a new algorithm may come close: Using nothing but written movie summaries, the AI can consistently tell which films will play well—or rottenly—to critics and audiences. If the model can be further refined, it could one day help producers predict whether a movie will be a flop at the box office, before it’s even made.

To test several models, researchers used plot summaries of 42,306 movies from all over the world, many collected from Wikipedia. The models broke up the summaries by sentence and used something called sentiment analysis to analyze each one. Sentences considered “positive,” such as “Thor loves his hammer,” would receive a rating closer to one. And sentences that were considered “negative,” like “Thor gets in a fight,” would be rated closer to negative one.

Artificial intelligence predicts which movies will succeed—and fail—simply from plot summaries

Aug 2, 2019, 1:20pm UTC
http://www.sciencemag.org/news/2019/08/artificial-intelligence-predicts-which-movies-will-succeed-and-fail-simply-plot > Artificial intelligence (AI) still can’t see the future, but a new algorithm may come close: Using nothing but written movie summaries, the AI can consistently tell which films will play well—or rottenly—to critics and audiences. If the model can be further refined, it could one day help producers predict whether a movie will be a flop at the box office, before it’s even made. > To test several models, researchers used plot summaries of 42,306 movies from all over the world, many collected from Wikipedia. The models broke up the summaries by sentence and used something called sentiment analysis to analyze each one. Sentences considered “positive,” such as “Thor loves his hammer,” would receive a rating closer to one. And sentences that were considered “negative,” like “Thor gets in a fight,” would be rated closer to negative one.