From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series

author: Brendan O'Connor, Machine Learning Department, School of Computer Science, Carnegie Mellon University
published: June 29, 2010,   recorded: May 2010,   views: 385
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Description

We connect measures of public opinion measured from polls with sentiment measured from text. We analyze several surveys on consumer confidence and political opinion over the 2008 to 2009 period, and find they correlate to sentiment word frequencies in contemporaneous Twitter messages. While our results vary across datasets, in several cases the correlations are as high as 80%, and capture important large-scale trends. The results highlight the potential of text streams as a substitute and supplement for traditional polling. consumer confidence and political opinion, and can also pre- dict future movements in the polls. We find that temporal smoothing is a critically important issue to support a successful model.

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