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Search for Sentiment

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Sentiment analysis meets search: presentation slides for Seth Grimes's talk Search for Sentiment at the Search Engine Meeting, April 27, 2010.

Sentiment analysis meets search: presentation slides for Seth Grimes's talk Search for Sentiment at the Search Engine Meeting, April 27, 2010.

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  • 1. Search for Sentiment
    Seth Grimes
    Alta Plana Corporation
    301-270-0795 -- http://altaplana.com
    Search Engine Meeting
    April 27, 2010
  • 2. Seth Grimes –
    Principal Consultant with Alta Plana Corporation.
    Contributing Editor, TechWeb’sIntelligentEnterprise.com.
    Channel Expert (text analytics), B-Eye-Network.com.
    Founding Chair, Sentiment Analysis Symposium, sentimentsymposium.com, and Text Analytics Summit, textanalyticsnews.com.
    Twitter: @SethGrimes, #SAS10
  • 3. Two assertions:
    Human communications are inherently subjective.
    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]
    Bernanke is doing a better job than Greenspan.
    www.google.com/publicdata
  • 5.
  • 6. 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?
    Who are opinion leaders?
    How does sentiment propagate across multiple channels?
  • 7. Is sentiment a search problem?
  • 8. Information access w/structure, sentiment:
    User intent?
    Sentiment
    Sentiment+
  • 9. “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.
  • 11. For better information access, understand user intent.
    User intent?
  • 12. We have a decision support need. We=
    Consumers
    Marketers
    Competitors
    Managers
    Decision support requires tools beyond general-purpose search/information access…
  • 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. Counting -- clicks, not even keywords -- leaves you wondering Why? and So What?
  • 15. “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
  • 16. 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.
    Intensity.
  • 17. 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…
  • 18. Sentiment sources (broadly):
    News
    Social media
    Enterprise feedback
    Consumption models:
    Push
    Pull (a.k.a. search)
    General search engine
    Siloed/vertical search interface
    Application embedded
    Widgets/gadgets
  • 19.
  • 20. Rated negative?
  • 21. Manual focus
    ???
  • 22. 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”)
  • 23. Claim: You fall far short with (only) --
    Doc-level analysis.
    Keyword-based analysis.
    For text, you need strong natural language processing (NLP) for information extraction:
    “A direct opinion is a quintuple (oj, fjk, ooijkl, hi, tl), where oj is an object, fjk is a feature of the object oj, ooijkl is the orientation or polarity of the opinion on feature fjk of object oj, hi is the opinion holder and tl is the time when the opinion is expressed by hi.” [Liu 2010]
    … index at will!
  • 24. Boost accuracy via ratings & classification:
  • 25. Next slides have a few more examples.
    A Jodangeembeddable “gadget.”
    Newssift.com, a now defunct media portal from the Financial Times Group.
  • 26.
  • 27.
  • 28. 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”
  • 29. 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.
  • 30. Questions?
    Comments?

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