Abstract:
Movie rating is a measure of viewer’s reaction to
movie performance at the box office and also a key feature
to garner publicity. Movie ratings are feedback measures
given by a subset of the audience voluntarily. If the degree
of effect on the human mindset can be measured through
real-time behavior analyzing and rated, the results can help
film houses to understand the secret of generating a commercial
success movie. Prediction of movie ratings is a complex problem.
Viewers, producers, directors, and production houses are
curious about how a given movie will perform in theatres
with different customer segments. Research works have been
carried out relating to movie rating prediction using social
networking, blogs articles, but much less has been explored by
the consumer behavioral data and attributes while watching a
movie continuously and using emotions and body movement
dimensions [1 – 4], [7], [12]. We created an audience footage data
set and transformed it into numerical feature data representing
the audience’s behavior. Prepossessing and machine learning
approaches were applied to build an efficient model that can
predict the movies’ popularity.
