Seinfeld, Big Data and Measuring the Internet’s
Emotional Landscape
by
Aleszu Bajak
[1]
March 16, 2015
Tom's Restaurant is a Seinfeld icon. Could the show give us insight into the Internet's
emotional landscape? Photo by
Mercedes Bugarin
[2]
and reused here with Creative Commons
license.
“I’m out there Jerry, and I’m lovin’ every minute of it!” Kramer, Jerry Seinfeld’s wacky
neighbor, would roar as he walked into a scene. Kramer was also the television show’s
happiest character, according to a mathematical analysis of the sitcom’s scripts conducted by
Chris Danforth and Peter Dodds, University of Vermont mathematicians.
The pair has been applying a ranking algorithm they’ve developed at UVM to rank the
“happiness” of words in a large dataset. “Seinfeld” scripts were a fun trial run. Their
happiness ranking system, which they and their colleagues at UVM’s Computational Story
Lab have dubbed the Hedonometer, has been used to analyze Google searches, Twitter
messages, movie scripts and song lyrics from around the world.
"Could a statistical approach using big data be a better barometer for reading cultural
mood?"
Their work has applications for journalists who want to tap big data for stories with more
firepower.
“The goal for most good data scientists these days is developing tools to discern which
stories pulled from big data are scientifically defensible,” Danforth told Storybench.
“Journalists will often use an anecdotal tweet to help personalize a story. We’re hoping that
our instrument can be used to quantify the bigger picture, the emotional temperature
surrounding public opinion.”
Analyzing “Seinfeld” is a playful application of a powerful tool the mathematicians
have developed to study positivity in language. Their argument is simple: The
current methods for measuring mood or positivity vary widely across surveys,
questionnaires, polls and other studies. Could a statistical approach using big data be a
better barometer for reading cultural mood?
Ranking words, reading moods
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Posted 17 Jul 2017 07:07 AM
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