Artificial intelligence predicts which movies will succeed—and fail—simply from plot summaries
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.
Generally, successful movies such as 1951’s Alice in Wonderland—which scored 80% on the movie-rating website Rotten Tomatoes—have frequent fluctuations in sentiment; unsuccessful ones, such as 2009’s The Limits of Control, fluctuate less. It’s not important whether the films begin or end happily, the researchers say. What’s important is that the sentiments change frequently.
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