David Cummings
Haruki Oh
Ningxuan (Jason) Wang

• Motivation: People spend millions of dollars on polling every year: politics, economy, entertainment
• Millions of posts on Twitter every day
• Can we model public opinion using tweets?
• Data: 476 million tweets from June to December
2009, courtesy of Jure Lescovec
• Public polls from The Gallup Organization (presidential approval, economic confidence) and Rasmussen
Reports (generic Congressional ballot)
• Goal: high correlation with public opinion polls
• All correlation figures for 6-day smoothing window

• The simplest metric: percentage of tweets that mention a given topic in a certain time window
• Moderate negative correlation (-36.3%, -35.7%) for economy and Congressional ballot: mention things you want to complain about more often
• Higher correlation (52.4%) for Obama
Presidential Approval: Volume Metric
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• Can we distinguish between positive and negative sentiment of tweets?
• University of Pennsylvania OpinionFinder subjective polarity lexicon
• “conceited” strong negative -10
• “ironic” weak negative -5
• “trendy” weak positive +5
• “illuminating” strong positive +10
• Sum word scores for a tweet to classify it as positive, negative, or neutral; then subtract negative counts from positive counts and normalize over window

• Good results on economic confidence: 60.4% correlation, 70.1% correlation on 15-day window
• Poor performance on presidential approval and
Congressional ballot: -24.5% and 21.5% correlation respectively
• Sentiment about politics expressed differently?
Economic Confidence: Generic Sentiment
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• Train three language models (positive, negative, and neutral) on hand-classified data
• Classify each tweet according to the language model that affords it the highest probability
• Applied for the case of Obama: manually classified
3,633 tweets
• “can we all talk about how awesome Obama is?”
• “that Obama sticker on your car might as well say ‘Yes
I’m stupid’ #tcot #iamthemob #teaparty #glennbeck”
• Then we tested the language models: best performer was a linearly interpolated bigram model

• Much-improved results on presidential approval:
49.4% correlation
• Throwing out retweets and duplicate tweets helps a little more: 55.9% correlation
• Finally, combining both volume and LM-based sentiment gives best results: 63.3% correlation, or
69.6% correlation on a 15-day window
Presidential Approval: LM-based Classification
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