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Case Study: Monitor Governor Election 2010

Monitor Setup

On SWI server, create topic list "Governor Election 2010" and then add a topic for each governor candidate for all the state races.  The candiate names are from Politics Daily.

State    Democrat    Republican

California    Jerry Brown    Meg Whitman
Oregon    John Kitzhaber    Chris Dudley
Idaho    Keith Allred    Butch Otter
Nevada    Rory Reid    Brian Sandoval
Utah    Peter Corroon    Gary Herbert
Arizona    Terry Goddard    Jan Brewer
Wyoming    Leslie Petersen    Matt Mead
Colorado    John Hickenlooper    Dan Maes
New Mexico    Diane Denish    Susana Martinez
South Dakota    Scott Heidepriem    Dennis Daugaard
Nebraska    Mike Meister    Dave Heineman
Kansas    Tom Holland    Sam Brownback
Oklahoma    Jari Askins    Mary Fallin
Texas    Bill White    Rick Perry
Minnesota    Mark Dayton    Tom Emmer
Iowa    Chet Culver    Terry Branstad
Arkansas    Mike Beebe    Jim Keet
Michigan    Virg Bernero    Rick Snyder
Wisconsin    Tom Barrett    Scott Walker
Illinois    Pat Quinn    Bill Brady
Tennessee    Mike McWherter    Bill Haslam
Alabama    Ron Sparks    Robert Bentley
Georgia    Roy Barnes    Nathan Deal
South Carolina    Vincent Sheheen    Nikki Haley
Florida    Alex Sink    Rick Scott
Ohio    Ted Strickland    John Kasich
Pennsylvania    Dan Onorato    Tom Corbett
Maryland    Martin O'Malley    Robert Ehrlich
New York    Andrew Cuomo    Carl Paladino
Connecticut    Dan Malloy    Tom Foley
Massachusetts    Deval Patrick    Charlie Baker
Vermont    Peter Shumlin    Brian Dubie
New Hampshire    John Lynch    John Stephen
Maine    Libby Mitchell    Paul LaPage


Monitoring

Twitter conversations and blogs are collected from Twitter and the blogsphere.

Categorizing Social Conversations

Set up categories relevant to election:
  • Positive sentiment
  • Negative sentiment
  • Issues
  • Campaign
  • Unknown
  • Exclusion

For each candidate, tweets are clustered for each day starting from 10/27/2010. Read through the clusters and manually assign a category to each cluster read. For those conversations with clear feature such as key statement or phrase, copy and add the feature to the category assigned.  As you manually assign categories, SWI server is learning from the features in real time and does automatic categorization after each new feature is added.  This automatic categorization helps you categorize the tweets faster.

Finding

California race: The sentiment difference is startling on Twitter. From the randomly selected tweets since 10/27, the ratio of positive to negative sentiments is 1.71 for Jerry Brown and 0.32 for Meg Whitman.

Positive/Negative sentiment ratio: 
Jerry Brown 1.71   
Meg Whitman 0.32



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