Download star Power Movie analytics engages Bentley research team By

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Movie analytics engages Bentley research team
By Kristen Walsh
In the 2015 Oscar season, data science went Hollywood for a team of
Bentley PhD candidates. Their research on analytics techniques explored a
must-have for the silver screen and computer screen alike: high-impact
visuals.
“Visualization is an important component used for understanding the
dynamics of any network, but it has traditionally been difficult when it
comes to movie analytics,” says Dominique Haughton, professor of
mathematical sciences and global studies. “Researchers have struggled with
a way to visualize very large data sets, as typical tools run into problems.”
Project principals were Mark-David McLaughlin, Kevin Mentzer and
Changan Zhang — students in the PhD Quant V course that Haughton
teaches. They applied different analytics techniques to see which
generated the best charts, graphs and other means of representing large,
complex data sets. Information for the test case came from the Internet
Movie Database (IMDb), whose network of co-stars includes 2.6 million
actors with upward of a billion connections.
They compared a “k-core” approach to visualizing data with a more
traditional technique to see which would best illustrate connections and
degrees of separation among co-stars. How many times has Tom Hanks
worked with Meg Ryan?
And the Oscar goes to … k-core. “We were able to illustrate that k-core
can overcome limitations, such as memory issues, that arise from
processing huge amounts of data,” says Haughton. “Our approach
presented findings in a visually appealing way.”
PLOT POINTS
The research explored other aspects of movie analytics, a domain that
Mentzer says is understudied. For example, they looked at online
predictions of Oscar winners and developed a how-to guide for text mining
online movie reviews related to the Academy Awards.
“An interesting finding with movie reviews is that movie complexity
matters, but only to a certain degree,” explains Mentzer. “In order to win
Best Picture, for example, you need a plot that appeals to many different
groups for different reasons — too simple a plot and you don’t have a large
enough base of support, but too complex you lose votes because people
start getting confused.”
AND . . . ACTION!
The movie analytics project follows a longstanding model of faculty-student
collaboration at Bentley. Her belief in such partnerships inspired Haughton
to co-found the Data Analytics Research Team (DART). Students and
professors use a wide range of analytical and modeling techniques to
investigate data sets in areas such as global studies and living standards,
marketing, health care, media and finance.
“Analytics has a different flavor now, as more and more people recognize
the importance of the data and the usefulness of the tools,” says DART
member David Oury. The lecturer in mathematical sciences is
spearheading a campus-based data lab with software and hardware to
support course curriculum and DART research. “Students and faculty are
using analytics technologies to explore how data can impact business and
operations, and how it can help create more productive and efficient
procedures.”
And Hollywood take note: Researchers recognize the need to present big
data in ways that non-scientists can understand and appreciate. For
example, to showcase the movie analytics findings, Haughton hosted an
on-campus Oscar party complete with red carpet, replica trophies and
proper attire. Of course the event featured flashy visuals: a laser show set
to Billy Joel’s It’s Still Rock ‘n Roll to Me.
Haughton credits DART and similar partnerships for “an impressive
amount of cross-disciplinary co-publication by students and faculty. There
are clear connections among areas such as math, geography, economics,
sociology, statistics, global studies, marketing and computer science.
Those are strong measures of success.”
The research team’s monograph — Movie Analytics: A Hollywood
Introduction to Big Data -— was published in November as part of the
SpringerBriefs in Statistics series.