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Sentimental Market Segmentation
 

Sentimental Market Segmentation

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Talk on our new technology for sentimental market segmentation, from 2010 Sentiment Analysis Symposium, NYC (http://sentimentsymposium.com).

Talk on our new technology for sentimental market segmentation, from 2010 Sentiment Analysis Symposium, NYC (http://sentimentsymposium.com).

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    Sentimental Market Segmentation Sentimental Market Segmentation Presentation Transcript

    • Sentimental Market Segmentation Shlomo Argamon, Ph.D. CEO & Cofounder, Subtext3 Based on a presentation given at Sentiment Analysis Symposium April 13, 2010, New York, NY
    • Lots of sentiment analysis… http://www.subtext3.com
      • Why??
      Lots of sentiment analysis… http://www.subtext3.com
    • Why sentiment analysis… http://www.subtext3.com What are they thinking? What do they want? What will they buy?
    • Where ’s the ROI?
      • What should I fix?
        • Find comparatively negative aspects of my product or positive areas about competitors ’
      • How ’m I doing?
        • Examine sentiment trends to examine effects of marketing or new products
      • Where ’s the action?
        • Find the customers ’ unfulfilled needs
      http://www.subtext3.com
    • More Generally… http://www.subtext3.com ? ? ? The Market (potential) Customers
    • Customer Model http://www.subtext3.com Perceptions Choices Products & Features Advertising Potential Customer Opinions Texts Needs & Wants
    • Customer Model http://www.subtext3.com Perceptions Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Choices
    • Customer Model http://www.subtext3.com Perceptions Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Choices
    • Customer Model http://www.subtext3.com Perceptions Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Choices
    • Customer Model http://www.subtext3.com Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Perceptions Perceptions Perceptions Choices
    • Customer Model http://www.subtext3.com Products & Features Advertising Opinions Texts Needs & Wants Perceptions Perceptions Perceptions Potential Customer Potential Customer Potential Customer Potential Customer Choices
    • Customer Model http://www.subtext3.com Products & Features Advertising Opinions Texts Needs & Wants Perceptions Perceptions Perceptions Potential Customer Potential Customer Potential Customer Potential Customer Choices
    • Customer Model http://www.subtext3.com Products & Features Advertising Opinions Texts Needs & Wants Perceptions Perceptions Perceptions Potential Customer Potential Customer Potential Customer Potential Customer Choices ?
    • Market Segmentation
      • Product/brand segmentation
        • What products are close to which?
        • Based on customer needs and perceptions , not product features
      • Customer/community segmentation
        • Meaningful subsets of potential customers
        • Relative to a given market!
        • Know their characteristics
      http://www.subtext3.com
    • Perceptual Maps http://www.subtext3.com
    • Community Maps http://www.subtext3.com
    • Understand The Community
      • Not just what they are saying…
          • Who is saying it?
            • What groups of people have similar opinions (about X)?
            • What kinds of people are they?
          • How do they see things?
            • How do they group products, brands, or features?
      http://www.subtext3.com
    • Varieties of Sentiment Analysis http://www.subtext3.com
    • http://www.subtext3.com Document Filter ( topic, source ) Sentiment Classifier Trend Snapshot Multidimensional
    • http://www.subtext3.com Topic Classifier Topic/Sentiment Correlation Trend Snapshot Multidimensional Sentiment Finder
    • http://www.subtext3.com Target Finder Target/Sentiment Correlation Trend Snapshot Multidimensional Sentiment Finder
    • But…
      • Who are they and what do they think??
      • We still need…
      • More detail on their opinions
      • Profiles of the writers
      http://www.subtext3.com
    • http://www.subtext3.com Detailed Sentiment Finder Target Finder Sentiment Complexes Demographic Profiling Demographic Trends Perceptual Map Customer Map Demographic Profiles
    • Detailed Sentiment Finding
      • Based on research in the Linguistic Cognition Lab at Illinois Institute of Technology
      • http://lingcog.iit.edu
      http://www.subtext3.com
    • Different kinds of sentiment http://www.subtext3.com
    • Different kinds of sentiment http://www.subtext3.com This unique dog or chicken house was made with green practices in mind.
      • Positive
      • Appreciation
      • Valuation
      Oh, and on principle I hate extremists / zealots of any ilk.
      • Negative
      • Affect
      • Affection
      ...if your site looks a complete mess then you will loose [sic] that visitor for good.
      • Negative
      • Appreciation
      • Quality
      Oh and Spooks, … with this really tough and sexy old lady in it, kicking ass.
      • Positive
      • Judgment
      • Tenacity
    • Syntactic connections http://www.subtext3.com
    • More sentiment expressions
      • [I] evaluator [couldn ’t] polarity bring myself to [like] attitude [him] target .
      • [It] target-1 is [not] polarity [as [good] attitude as] comparator [the Minolta D7] target-2 .
      • [Gap.Com] target is an [excellent] attitude example of [a retailer] superordinate [using its online shopping store as an extension and expansion of its retailing] aspect .
      http://www.subtext3.com
    • Detailed sentiment finding http://www.subtext3.com Complex Sentiment Expressions Find Chunks ( attitudes, targets, hinges,… ) Chunks Expression Linkage Disambiguation Texts Lexicon Linkage Rules Dependency Parsing Syntactic Relations
    • Demographic Profiling http://www.subtext3.com
    • Authorship Profiling
      • Infer things about the author from the style of the language…
        • Gender
        • Age
        • Native language
        • Personality type
        • Education level
        • Etc…
      http://www.subtext3.com
    • Capturing language style
      • Linguistic variation independent of the topic
      • Function words
      • Parts-of-speech
      • Syntactic structures
      • Morphology
      • Linguistic complexity
      • Vocabulary size
      • Mistakes
      • Slang
      http://www.subtext3.com
    • Male/Female Classification
      • 20th Century narrative fiction: 79%
      • 20th Century non-fiction: 83%
      • 21st Century blogs: 77%
      • 17th-19th Century French lit.: 76%
      http://www.subtext3.com Male Features Female Features the, this, that, those, as, one, of, to, prepositions, adjectives, numbers she, for, with, not, and, in, I, you, pronouns, present-tense-verbs
    • Age Classification
      • Blogs, classified as “teens”, “twenties”, “thirties-plus”: 75%
      http://www.subtext3.com Word 10s 20s 30s maths 105 3 2 homework 137 18 15 bored 384 111 47 sis 74 26 10 boring 369 102 63 awesome 292 128 57 mum 125 41 23 crappy 46 28 11 mad 216 80 53 dumb 89 45 22 Word 10s 20s 30s semester 22 44 18 apartment 18 123 55 drunk 77 88 41 beer 32 115 70 student 65 98 61 album 64 84 56 college 151 192 131 someday 35 40 28 dating 31 52 37 bar 45 153 111 Word 10s 20s 30s marriage 27 83 141 development 16 50 82 campaign 14 38 70 tax 14 38 72 local 38 118 185 democratic 13 29 59 son 51 92 237 systems 12 36 55 provide 15 54 69 workers 10 35 46
    • Other dimensions
      • Native language: ~80%
      • Personality:
        • Neuroticism: ~68%
        • Extraversion: ~55-70%
      http://www.subtext3.com
    • Prototype Results http://www.subtext3.com
    • Simple Prototype
      • 53,983 blog snippets from the ICWSM task corpus (Aug-Sep, 2008)
      • 268,665 sentiment expressions found
      • Examples:
        • … I think that Sarah [Palin] target would be a [terrible] attitude [vice president] superordinate
        • … [the game] target was [too simplistic] attitude [to serve as proper material for argument] aspect …
      http://www.subtext3.com
    • Authorship Profiling
      • Gender ( Male/Female )
      • Age ( Younger/Older )
        • Based on features from previous studies
      • Education level ( LowerEd, MediumEd, HigherEd )
        • Based on linguistic complexity
      http://www.subtext3.com
    • Trend Analysis http://www.subtext3.com
    • Apartment sentiment by gender http://www.subtext3.com
    • Apartments - gender difference http://www.subtext3.com
    • McCain & Palin - gender difference http://www.subtext3.com
    • Market Mapping http://www.subtext3.com
    • Perceptual Map - Relationships
    • Perceptual Map - Relationships
    • Community Map - Relationships ?
    • Community Map - Marriage
    • Perceptual Map - Issues
    • Perceptual Map - Issues
    • Community Map - Issues
    • Sentimental Market Segmentation
      • Construct perceptual and community maps, by:
        • Detailed extraction of sentiment expressions
          • Semantic and structural detail
        • Authorship profiling
          • Tells us what kinds of people are writing which opinions
          • (Also need to attribute third-party sources…)
        • Dimensionality reduction over author opinions and profiles (PCA, MDS, etc.)
      http://www.subtext3.com
    • Thank you
      • Contact us:
      • Shlomo Argamon
      • [email_address]
      • @ShlomoArgamon
      • http://www.subtext3.com
      http://www.subtext3.com