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Carnegie Mellon Study of Twitter Sentiments Yields Results Similar to Public Opinion Polls
PITTSBURGH - Computer analysis of sentiments expressed in a billion Twitter messages during 2008-2009 yielded measures of consumer confidence and of presidential job approval similar to those of well-established public opinion polls, Carnegie Mellon University researchers report.
The findings suggest that analyzing the text found in streams of tweets could become a cheap, rapid means of gauging public opinion on at least some subjects, said Noah Smith, assistant professor of language technologies and machine learning in the School of Computer Science. But tools for extracting public opinion from social media text are still crude and social media remain in their infancy, he cautioned, so the extent to which these methods could replace or supplement traditional polling is still unknown.
"With seven million or more messages being tweeted each day, this data stream potentially allows us to take the temperature of the population very quickly," Smith said. "The results are noisy, as are the results of polls. Opinion pollsters have learned to compensate for these distortions, while we're still trying to identify and understand the noise in our data. Given that, I'm excited that we get any signal at all from social media that correlates with the polls."
The study findings will be presented May 25 at the Association for the Advancement of Artificial Intelligence's International Conference on Weblogs and Social Media in Washington, D.C.
Download a copy of the paper at www.cs.cmu.edu/~nasmith/.
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