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Seth Grimes - Sentiment in Social Media

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  • 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
  • 2.
  • 3. Three assertions:
    Human communications, online & off, are inherently subjective.
    Online facts & opinions have business value.
    Opinion often masquerades as Fact.
  • 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
  • 5.
  • 6.
  • 7. Information access w/structure, sentiment:
    User intent?
    Sentiment
    Sentiment+
  • 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
  • 9.
  • 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. 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?
    • 12. (How) Can we link opinion & intent?
    Who are opinion leaders?
    How does sentiment propagate across multiple channels?
  • 13. Counting term hits, in one source, at the doc level, doesn’t take you far...
    Good or bad? What’s behind the posts?
  • 14. “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.
  • 16. Rated negative?
  • 17. ???
  • 18. 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
  • 19. 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”)
  • 20. Next slides have a few more examples.
    SAS Social Media Analytics.
    Clarabridge Social Media Analysis.
    Crimson Hexagon VoxTrot.
    Clarabridge sentiment analysis.
    A Jodangeembeddable “gadget.”
    Newssift.com, a now defunct media portal from the Financial Times Group.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27. 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”
  • 28. HappySadAngry
    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 LiveJournalmood labels.
  • 29. Questions?
    Comments?