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


  • Brendan O'Connor Carnegie Mellon University
  • Ramnath Balasubramanyan Carnegie Mellon University
  • Bryan Routledge Carnegie Mellon University
  • Noah Smith Carnegie Mellon University


sentiment analysis, surveys, opinion polls, consumer confidence, political elections, text mining, blogs, social media, twitter


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 contempora- neous 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 re- sults highlight the potential of text streams as a substi- tute 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 suc- cessful model.




How to Cite

O’Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. (2010). From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 122-129. Retrieved from