leveraging social media data as real-time indicators of x
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Leveraging Social Media Data as Real-Time Indicators of X. September 7, 2012. Outline . Opportunities and Challenges in Big Data Sentiment Tracking Methods Validating Results Case Study: Heat Mapping Case Study- Economic forecasting. The Energy Market as a Metaphor. Big Data. Energy. - PowerPoint PPT PresentationTRANSCRIPT
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Copyright © 2012 IHS Inc. All Rights Reserved. Company Confidential
Leveraging Social Media Data as Real-Time Indicators of X
September 7, 2012
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Outline 1. Opportunities and Challenges in Big Data2. Sentiment Tracking• Methods• Validating Results
3. Case Study: Heat Mapping4. Case Study- Economic forecasting
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The Energy Market as a Metaphor
Big Data• Variety of Sources• High Volume • Converting to valuable
product is a particular challenge that varies on topic area
Energy• Variety of sources• Tremendous potential
value• Extraction, delivery and
conversion a challenge
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Key Product Area: Real-time sentiment
• Video of Twittermood
Starting Point – Pulse of the Nation – “Twittermood” (2010)
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Analysis Approach and Methods• Sentiment analysis well-studied in the academic literature
• NLP techniques often expensive• Luckily, Twitter messages are short
• First cut: Look for occurrences of words (Use ANEW or EMOT word list)
• Second cut: associate words and emoticons
TWEET EXAMPLES:
My new Ford Focus needs repair again, so frustrating Just back from road trip, loved the comfort of my new Accord Visualize trends over course of day, week and can overlay geography with location of users
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Mood Score
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• Amazon’s Mechanical Turk Test is used to compare our proprietary sentiment scoring of tweets with those of human raters.
• Best inter-rater reliability correlation between humans is 0.80 (gold standard).
• Our test of our algorithm’s sentiment assignment resulted in Pearson and Spearman correlations of ~0.75.
OPTIMIZING SENTIMENT ACCURACY
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We adjust the daily average by population size and gender make-up from census data. A tweet from some state can be weighted by the relative proportion of that state’s population to the U.S. It can be further weighted by gender ratio within the state.
CONTROLLING FOR SAMPLE BIASES
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Correcting for Population Levels
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Compared U.S. data to data of 4 European countries (using different languages) over the past month.
One-week moving average shows broad trends, with some similarity in overall shape, differences in degree of variation.
MOVING BEYOND U.S. BORDERS
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The Revolution Will BE Tweeted
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Central London
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AlcoholCompilation of Trigger Messages
Top Tweets"...and another round of Chivasgoes down!"'....we are pissed...""...bloody City, chokes again, send me another, bartender..."
Search for…
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Sentiment + the economy• Validity of mood scores: • Internal: Our algorithm does an extremely good job of
matching human scoring of mood• External: When we construct a ‘sentiment’ measure
from the individual tweets, this measure moves in ways that are not surprising. High on Christmas, low after earthquakes• External: How is this sentiment measure correlated with
economic activity?
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State-level analysis
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Unemployment + mood
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Sentiment + the economy• Takeaway: sentiment measure has some incremental
predictive power for state-level changes in unemployment. • Evidence from financial markets: (daily) increases in
sentiment measure followed by increases in market volatility.• Overall: evidence for external validity of sentiment
indicator.