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Sentiment in Social Media: The Genie in the Bottle
 

Sentiment in Social Media: The Genie in the Bottle

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Presentation for Measuring Social Media, Boston, October 5, 2010, by Seth Grimes

Presentation for Measuring Social Media, Boston, October 5, 2010, by Seth Grimes

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    Sentiment in Social Media: The Genie in the Bottle Sentiment in Social Media: The Genie in the Bottle Presentation Transcript

    • Sentiment in Social Media: The Genie in the Bottle
      Seth Grimes
      Alta Plana Corporation
      301-270-0795 -- http://altaplana.com -- @sethgrimes
      Monitoring Social Media -- Boston
      October 5, 2010
    • Three assertions:
      Human communications, online & off, are inherently subjective.
      Online facts & opinions have business value.
      Opinion often masquerades as Fact.
    • 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
    • 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?
    • Information access w/structure, sentiment:
      User intent?
      Sentiment
      Sentiment+
    • “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
    • For better information access, understand user intent.
      User intent?
    • We have a decision support need. We=
      Consumers
      Marketers
      Competitors
      Managers
      Decision support requires tools beyond general-purpose search/information access…
    • Counting term hits, in one source, at the doc level, doesn’t take you far...
      Good or bad? What’s behind the posts?
    • Counting -- clicks, not even keywords -- leaves you wondering Why? and So What?
    • “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 or opinion mining is the computational study of opinions, sentiments and emotions expressed in text… An opinion on a feature f is a positive or negative view, attitude, emotion or appraisal on f from an opinion holder.”
      -- Bing Liu, 2010, “Sentiment Analysis and Subjectivity,” in Handbook of Natural Language Processing
    • Sentiment analysis turns attitudes into data.
      Ingredients:
      Structured and unstructured sources.
      Subjectivity – WW&H used over 8,000 clues.
      Polarity: positive, negative, (both,) or neutral.
      • Other classifiers?
      Intensity.
    • There are many complications. Simplified:
      Sentiment may be of interest at multiple levels.
      Corpus / data space, i.e., across multiple sources.
      Document.
      Statement / sentence.
      Entity / topic / concept.
      Human language is noisy and chaotic!
      Jargon, slang, irony, ambiguity, anaphora, polysemy, synonymy, etc.
      Context is key. Discourse analysis comes into play.
      Must distinguish the sentiment holder from the object: Greenspan said the recession will…
    • Sentiment sources (broadly):
      News
      Social media
      Enterprise feedback
      (To me, it’s all social.)
      Consumption models:
      General search engine
      Siloed/vertical search interfaces, a.k.a. monitoring tools
      Application embedded
      Widgets/gadgets
    • Rated negative?
    • Manual focus
      ???
    • 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”)
    • 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
    • Next slides have a few more examples.
      SAS Social Media Analytics.
      Clarabridge Social Media Analysis.
      Crimson Hexagon VoxTrot.
      Clarabridgesentiment analysis.
      A Jodangeembeddable “gadget.”
      Newssift.com, a now defunct media portal from the Financial Times Group.
    • 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”
    • 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.
    • Questions?
      Comments?