  Television is an ever-evolving multi billion dollar industry. The success of
a television show in an increasingly technological society is a vast
multi-variable formula. The art of success is not just something that happens,
but is studied, replicated, and applied. Hollywood can be unpredictable
regarding success, as many movies and sitcoms that are hyped up and promise to
be a hit end up being box office failures and complete disappointments. In
current studies, linguistic exploration is being performed on the relationship
between Television series and target community of viewers. Having a decision
support system that can display sound and predictable results would be needed
to build confidence in the investment of a new TV series. The models presented
in this study use data to study and determine what makes a sitcom successful.
In this paper, we use descriptive and predictive modeling techniques to assess
the continuing success of television comedies: The Office, Big Bang Theory,
Arrested Development, Scrubs, and South Park. The factors that are tested for
statistical significance on episode ratings are character presence, director,
and writer. These statistics show that while characters are indeed crucial to
the shows themselves, the creation and direction of the shows pose implication
upon the ratings and therefore the success of the shows. We use machine
learning based forecasting models to accurately predict the success of shows.
The models represent a baseline to understanding the success of a television
show and how producers can increase the success of current television shows or
utilize this data in the creation of future shows. Due to the many factors that
go into a series, the empirical analysis in this work shows that there is no
one-fits-all model to forecast the rating or success of a television show.
