1. Sentiment in Social Media:
The Genie in the Bottle
Seth Grimes
Alta Plana Corporation
301-270-0795 -- http://altaplana.com -- @sethgrimes
Monitoring Social Media – New York
November 4, 2010

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Three
assertions:
1.Human
communications,
online & off, are
inherently
subjective.
2.Online facts &
opinions have
business value.
3.Opinion often
masquerades as
Fact.

4. Sentiment in Social Media 4
Facts and Feelings
The unemployment rate is 9.7%.
Unemployment is WAY TOO HIGH!!
The unemployment rate is higher than it was two years
ago (5.1%).
Former U.S. Federal Reserve Chairman Alan Greenspan
said on Tuesday that the global recession will "surely be
the longest and deepest" since the 1930s, adding that
the Obama administration's Troubled Asset Relief
Program will be insufficient to plug the yawning financial
gap. [Reuters, Feb 18, 2009] [underlining added]
Bernanke is doing a better job than Greenspan.
www.google.com/publicdata

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Information access w/structure, sentiment:
Sentiment+
Sentiment
User intent?

8. Sentiment in Social Media 8
“In this example, you can quickly see that the Drooling Dog
Bar B Q has gotten lots of positive reviews, and if you want
to see what other people have said about the restaurant,
clicking this result is a good choice.”
-- http://googleblog.blogspot.com/2009/05/more-search-options-and-other-updates.html
“In the recap of [Searchology] from Google’s Matt Cutts, he
tells us that: ‘If you sort by reviews, Google will perform
sentiment analysis and highlight interesting comments.’
-- Bill Slawski, “Google's New Review Search Option and Sentiment Analysis,”
http://www.seobythesea.com/?p=1488

10. Sentiment in Social Media 10
We have a decision support need. We=
Consumers
Marketers
Competitors
Managers
Decision support requires tools and
techniques beyond general-purpose
search/information access.

11. Sentiment in Social Media 11
Questions for business & government:
What are people saying? What’s hot/trending?
What are they saying about {topic|person|product} X?
... about X versus {topic|person|product} Y?
How has opinion about X and Y evolved?
How has opinion correlated with
{our|competitors’|general}
{news|marketing|sales|events}?
What’s behind opinion, the root causes?
• (How) Can we link opinions & transactions?
• (How) Can we link opinion & intent?
Who are opinion leaders?
How does sentiment propagate across multiple channels?

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Counting term hits, in one source, at the doc
level, doesn’t take you far...
Good or bad? What’s behind the posts?

13. Sentiment in Social Media 13
“Sentiment analysis is the task of identifying
positive and negative opinions, emotions,
and evaluations.”
-- Wilson, Wiebe & Hoffman, 2005, “Recognizing Contextual Polarity in
Phrase-Level Sentiment Analysis”
Sentiment analysis turns attitudes into data.
Ingredients:
News.
Social media.
Enterprise feedback.
Tools?

15. Rated
negative?

16. ???

17. Sentiment in Social Media 17
Claim: You fall far short with (only) --
Doc-level analysis:
• Need to look at features, opinion holders.
Keyword-based analysis.
• Need semantics.
Human-only analysis.
• Need the power of machines.
Machine-only analysis.
• Need the sensitivity of humans.
“Reading from text in general is a hard problem,
because it involves all of common sense knowledge.”
-- Expert systems pioneer Edward A. Feigenbaum

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An accuracy aside: [WWH 2005] describes an
inter-annotator agreement test.
10 documents w/ 447 subjective expressions. The
two annotators agree on 82% of cases.
Excluding of uncertain subjective expressions
(18%) boosts agreement to 90%.
(Wilson, Wiebe & Hoffman, 2005, “Recognizing Contextual Polarity in Phrase-Level
Sentiment Analysis”)

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Next slides have a few more examples.
SAS Social Media Analytics.
Clarabridge Social Media Analysis.
Crimson Hexagon VoxTrot.
Clarabridge sentiment analysis.
A Jodange embeddable “gadget.”
Newssift.com, a now defunct media portal from
the Financial Times Group.

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Beyond polarity: “We present a system that
adds an emotional dimension to an activity
that Internet users engage in frequently,
search..”
-- Sood & Vasserman & Hoffman, 2009, “ESSE: Exploring
Mood on the Web”

27. Sentiment in Social Media 27
Happy Sad Angry
Energetic Confused Aggravated
Bouncy Crappy Angry
Happy Crushed Bitchy
Hyper Depressed Enraged
Cheerful Distressed Infuriated
Ecstatic Envious Irate
Excited Gloomy Pissed off
Jubilant Guilty
Giddy Intimidated
Giggly Jealous
Lonely
Rejected
Sad
Scared
-----------------------
The three prominent mood groups that
emerged from K-Means Clustering on
the set of LiveJournal mood labels.

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Questions?
Comments?