Abstract
ABSTRACT We employed a machine learning structure to examine the relationships between word choice in Internet Movie Database (IMDB) comedy movie descriptions and overall performance. Our measures of success were ticket sales, user ratings, and Metacritic scores. We used linear regressions, along with recurrent neural networks implementing a Long Short-Term Memory framework, for textual sentiment analysis. Employing conservative p-values, our results revealed the possible influence of gender bias in movies that favoured male-centric themes, as well as negative effects for holiday comedies, paranormal movies, and crime films.
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