Sentimental Market Segmentation Shlomo Argamon Illinois Institute of Technology Department of Computer Science Chicago, IL...
Sentiment Analysis…
<ul><li>Why?? </li></ul>Sentiment Analysis…
Sentiment Analysis… What are they thinking? What do they want? What will they buy?
Where’s the ROI? <ul><li>What should I fix? </li></ul><ul><ul><li>Find comparatively negative aspects of my product or pos...
More Generally… The Market (potential) Customers
Customer Model Perceptions Choices Products & Features Advertising Potential Customer Opinions Texts Needs & Wants
Customer Model Perceptions Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Choices
Customer Model Perceptions Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Choices
Customer Model Perceptions Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Choices
Customer Model Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Perceptions Perceptions Per...
Customer Model Products & Features Advertising Opinions Texts Needs & Wants Perceptions Perceptions Perceptions Potential ...
Customer Model Products & Features Advertising Opinions Texts Needs & Wants Perceptions Perceptions Perceptions Potential ...
Customer Model Products & Features Advertising Opinions Texts Needs & Wants Perceptions Perceptions Perceptions Potential ...
Market Segmentation <ul><li>Product/brand segmentation </li></ul><ul><ul><li>What products are close to which? </li></ul><...
Perceptual Maps
Community   Maps
Understand The Community <ul><li>Not just  what  they are saying… </li></ul><ul><ul><ul><li>Who  is saying it? </li></ul><...
Sentiment Analysis
Document Filter ( topic, source ) Sentiment Classifier Trend Snapshot Multidimensional
Topic Classifier Topic/Sentiment Correlation Trend Snapshot Multidimensional Sentiment Finder
Target Finder Target/Sentiment Correlation Trend Snapshot Multidimensional Sentiment Finder
But… <ul><li>Who are they and what do they think?? </li></ul><ul><li>We still need… </li></ul><ul><li>More detail on their...
Detailed Sentiment Finder Target Finder Sentiment Complexes Authorship Profiling Demographic Trends Perceptual Map Custome...
Detailed Sentiment Finding
Detailed Sentiment Finding Complex Sentiment Expressions Find  Chunks ( attitudes, targets,  hinges,… ) Chunks Expression ...
Different kinds of sentiment
Syntactic Linkage
More complex patterns
Sentiment expressions <ul><li>[I] evaluator  [couldn’t] polarity  bring myself   to [like] attitude  [him] target . </li><...
Authorship Profiling
Authorship Profiling <ul><li>Infer things about the author from the style of the language… </li></ul><ul><ul><li>Gender </...
Capturing language style <ul><li>Linguistic variation  orthogonal to topic </li></ul><ul><li>Function words </li></ul><ul>...
Male/Female Classification <ul><li>20th Century narrative fiction: 79% </li></ul><ul><li>20th Century non-fiction: 83% </l...
Age Classification <ul><li>Blogs, classified as “teens”, “twenties”, “thirties-plus”: 75% </li></ul>22 45 89 dumb 53 80 21...
Other dimensions <ul><li>Native language: ~80% </li></ul><ul><li>Personality: </li></ul><ul><ul><li>Neuroticism: ~68% </li...
Prototype Results
Simple Prototype <ul><li>53,983 blog snippets from the ICWSM task corpus (Aug-Sep, 2008) </li></ul><ul><li>268,665 sentime...
Unsupervised Profiling <ul><li>Gender ( Male/Female ) </li></ul><ul><li>Age ( Younger/Older )  </li></ul><ul><ul><li>Based...
Trend Analysis
Apartment sentiment by gender
Apartments - gender difference
Sarah Palin - gender difference
Market Mapping
Perceptual Map - Relationships
Perceptual Map - Relationships
Perceptual Map - Issues
Perceptual Map - Issues
Community Map - Issues
Perceptual Map - Politicians
Community Map - Relationships
Community Map - Marriage
Sentimental Market Segmentation <ul><li>Construct perceptual and community maps, by: </li></ul><ul><ul><li>Detailed extrac...
Acknowledgments <ul><li>Sentiment analyzer: </li></ul><ul><ul><li>Ken Bloom  </li></ul></ul><ul><ul><li>with Navendu Garg ...
Thank you <ul><li>Contact: </li></ul><ul><li>Shlomo Argamon </li></ul><ul><li>[email_address] </li></ul><ul><li>@ShlomoArg...
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Sentimental Market Segmentation, Shlomo Argamon

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The usual goal of sentiment analysis is to provide numeric measures of positive or negative valence for brands, products, and commodities, which can be aggregated over time or geographical regions to analyze patterns and trends. Dr. Shlomo Argamon discusses some new methods he and his team are developing which extend this paradigm in two ways. First, their systems analyze more aspects of each individual sentiment expression, including different types of attitude ("unwieldy" vs. "unreliable"), comparisons ("X is better than Y"), evaluative trends ("X is improving"), and modality ("possibly" vs. "likely" vs. "definitely"). Secondly, they are combining sentiment analysis with their methods for automated authorship profiling, which label texts with author characteristics such as gender, age, native language, education level, and so forth. When this is done, a new type of analysis emerges: data mining can be used to find "sentimental market segments", discovering, for example, that opinion is trending upwards for males aged 20-30, but downwards 30-50 year-olds who did not attend college. He presents some of their research results and discuss the implications for future applications and developments in sentiment analytics.

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Sentimental Market Segmentation, Shlomo Argamon

  1. 1. Sentimental Market Segmentation Shlomo Argamon Illinois Institute of Technology Department of Computer Science Chicago, IL Sentiment Analysis Symposium April 13, 2010, New York, NY
  2. 2. Sentiment Analysis…
  3. 3. <ul><li>Why?? </li></ul>Sentiment Analysis…
  4. 4. Sentiment Analysis… What are they thinking? What do they want? What will they buy?
  5. 5. Where’s the ROI? <ul><li>What should I fix? </li></ul><ul><ul><li>Find comparatively negative aspects of my product or positive areas about competitors’ </li></ul></ul><ul><li>How’m I doing? </li></ul><ul><ul><li>Examine sentiment trends to examine effects of marketing or new products </li></ul></ul><ul><li>Where’s the action? </li></ul><ul><ul><li>Find the customers’ unfulfilled needs </li></ul></ul>
  6. 6. More Generally… The Market (potential) Customers
  7. 7. Customer Model Perceptions Choices Products & Features Advertising Potential Customer Opinions Texts Needs & Wants
  8. 8. Customer Model Perceptions Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Choices
  9. 9. Customer Model Perceptions Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Choices
  10. 10. Customer Model Perceptions Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Choices
  11. 11. Customer Model Products & Features Advertising Potential Customer Opinions Texts Needs & Wants Perceptions Perceptions Perceptions Choices
  12. 12. Customer Model Products & Features Advertising Opinions Texts Needs & Wants Perceptions Perceptions Perceptions Potential Customer Potential Customer Potential Customer Potential Customer Choices
  13. 13. Customer Model Products & Features Advertising Opinions Texts Needs & Wants Perceptions Perceptions Perceptions Potential Customer Potential Customer Potential Customer Potential Customer Choices
  14. 14. Customer Model Products & Features Advertising Opinions Texts Needs & Wants Perceptions Perceptions Perceptions Potential Customer Potential Customer Potential Customer Potential Customer Choices ?
  15. 15. Market Segmentation <ul><li>Product/brand segmentation </li></ul><ul><ul><li>What products are close to which? </li></ul></ul><ul><ul><li>Based on customer needs and perceptions , not product features </li></ul></ul><ul><li>Customer/community segmentation </li></ul><ul><ul><li>Meaningful subsets of potential customers </li></ul></ul><ul><ul><li>Relative to a given market! </li></ul></ul><ul><ul><li>Know their characteristics </li></ul></ul>
  16. 16. Perceptual Maps
  17. 17. Community Maps
  18. 18. Understand The Community <ul><li>Not just what they are saying… </li></ul><ul><ul><ul><li>Who is saying it? </li></ul></ul></ul><ul><ul><ul><ul><li>What groups of people have similar opinions (about X)? </li></ul></ul></ul></ul><ul><ul><ul><ul><li>What kinds of people are they? </li></ul></ul></ul></ul><ul><ul><ul><li>How do they see things? </li></ul></ul></ul><ul><ul><ul><ul><li>How do they group products, brands, or features? </li></ul></ul></ul></ul>
  19. 19. Sentiment Analysis
  20. 20. Document Filter ( topic, source ) Sentiment Classifier Trend Snapshot Multidimensional
  21. 21. Topic Classifier Topic/Sentiment Correlation Trend Snapshot Multidimensional Sentiment Finder
  22. 22. Target Finder Target/Sentiment Correlation Trend Snapshot Multidimensional Sentiment Finder
  23. 23. But… <ul><li>Who are they and what do they think?? </li></ul><ul><li>We still need… </li></ul><ul><li>More detail on their opinions </li></ul><ul><li>Profiles of the writers </li></ul>
  24. 24. Detailed Sentiment Finder Target Finder Sentiment Complexes Authorship Profiling Demographic Trends Perceptual Map Customer Map Demographic Profiles
  25. 25. Detailed Sentiment Finding
  26. 26. Detailed Sentiment Finding Complex Sentiment Expressions Find Chunks ( attitudes, targets, hinges,… ) Chunks Expression Linkage Disambiguation Texts Lexicon Linkage Rules Dependency Parsing Syntactic Relations
  27. 27. Different kinds of sentiment
  28. 28. Syntactic Linkage
  29. 29. More complex patterns
  30. 30. Sentiment expressions <ul><li>[I] evaluator [couldn’t] polarity bring myself to [like] attitude [him] target . </li></ul><ul><li>[It] target-1 is [not] polarity [as [good] attitude as] comparator [the Minolta D7] target-2 . </li></ul><ul><li>[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 . </li></ul>
  31. 31. Authorship Profiling
  32. 32. Authorship Profiling <ul><li>Infer things about the author from the style of the language… </li></ul><ul><ul><li>Gender </li></ul></ul><ul><ul><li>Age </li></ul></ul><ul><ul><li>Native language </li></ul></ul><ul><ul><li>Personality type </li></ul></ul><ul><ul><li>Education level </li></ul></ul><ul><ul><li>Etc… </li></ul></ul>
  33. 33. Capturing language style <ul><li>Linguistic variation orthogonal to topic </li></ul><ul><li>Function words </li></ul><ul><li>Parts-of-speech </li></ul><ul><li>Syntactic structures </li></ul><ul><li>Morphology </li></ul><ul><li>Linguistic complexity </li></ul><ul><li>Vocabulary size </li></ul><ul><li>Mistakes </li></ul><ul><li>Slang </li></ul>
  34. 34. Male/Female Classification <ul><li>20th Century narrative fiction: 79% </li></ul><ul><li>20th Century non-fiction: 83% </li></ul><ul><li>21st Century blogs: 77% </li></ul><ul><li>17th-19th Century French lit.: 76% </li></ul>she, for, with, not, and, in, I, you, pronouns, present-tense-verbs the, this, that, those, as, one, of, to, prepositions, adjectives, numbers Female Features Male Features
  35. 35. Age Classification <ul><li>Blogs, classified as “teens”, “twenties”, “thirties-plus”: 75% </li></ul>22 45 89 dumb 53 80 216 mad 11 28 46 crappy 23 41 125 mum 57 128 292 awesome 63 102 369 boring 10 26 74 sis 47 111 384 bored 15 18 137 homework 2 3 105 maths 30s 20s 10s Word 111 153 45 bar 37 52 31 dating 28 40 35 someday 131 192 151 college 56 84 64 album 61 98 65 student 70 115 32 beer 41 88 77 drunk 55 123 18 apartment 18 44 22 semester 30s 20s 10s Word 46 35 10 workers 69 54 15 provide 55 36 12 systems 237 92 51 son 59 29 13 democratic 185 118 38 local 72 38 14 tax 70 38 14 campaign 82 50 16 development 141 83 27 marriage 30s 20s 10s Word
  36. 36. Other dimensions <ul><li>Native language: ~80% </li></ul><ul><li>Personality: </li></ul><ul><ul><li>Neuroticism: ~68% </li></ul></ul><ul><ul><li>Extraversion: ~55-70% </li></ul></ul>
  37. 37. Prototype Results
  38. 38. Simple Prototype <ul><li>53,983 blog snippets from the ICWSM task corpus (Aug-Sep, 2008) </li></ul><ul><li>268,665 sentiment expressions found </li></ul><ul><li>Examples: </li></ul><ul><ul><li>…I think that Sarah [Palin] target would be a [terrible] attitude [vice president] superordinate </li></ul></ul><ul><ul><li>…[the game] target was [too simplistic] attitude [to serve as proper material for argument] aspect … </li></ul></ul>
  39. 39. Unsupervised Profiling <ul><li>Gender ( Male/Female ) </li></ul><ul><li>Age ( Younger/Older ) </li></ul><ul><ul><li>Based on features from previous studies </li></ul></ul><ul><li>Education level ( LowerEd, MediumEd, HigherEd ) </li></ul><ul><ul><li>Based on linguistic complexity </li></ul></ul>
  40. 40. Trend Analysis
  41. 41. Apartment sentiment by gender
  42. 42. Apartments - gender difference
  43. 43. Sarah Palin - gender difference
  44. 44. Market Mapping
  45. 45. Perceptual Map - Relationships
  46. 46. Perceptual Map - Relationships
  47. 47. Perceptual Map - Issues
  48. 48. Perceptual Map - Issues
  49. 49. Community Map - Issues
  50. 50. Perceptual Map - Politicians
  51. 51. Community Map - Relationships
  52. 52. Community Map - Marriage
  53. 53. Sentimental Market Segmentation <ul><li>Construct perceptual and community maps, by: </li></ul><ul><ul><li>Detailed extraction of sentiment expressions </li></ul></ul><ul><ul><ul><li>Semantic and structural detail </li></ul></ul></ul><ul><ul><li>Authorship profiling </li></ul></ul><ul><ul><ul><li>Tells us what kinds of people are writing which opinions </li></ul></ul></ul><ul><ul><ul><li>(Also need to attribute third-party sources…) </li></ul></ul></ul><ul><ul><li>Dimensionality reduction over author opinions and profiles (PCA, MDS, etc.) </li></ul></ul><ul><ul><li>Currently in process of obtaining IP protection </li></ul></ul>
  54. 54. Acknowledgments <ul><li>Sentiment analyzer: </li></ul><ul><ul><li>Ken Bloom </li></ul></ul><ul><ul><li>with Navendu Garg and Casey Whitelaw </li></ul></ul><ul><li>Authorship profiling: </li></ul><ul><ul><li>Moshe Koppel and James W. Pennebaker </li></ul></ul><ul><ul><li>with Jonathan Schler and Sterling Stein </li></ul></ul>
  55. 55. Thank you <ul><li>Contact: </li></ul><ul><li>Shlomo Argamon </li></ul><ul><li>[email_address] </li></ul><ul><li>@ShlomoArgamon </li></ul><ul><li>http://lingcog.iit.edu </li></ul>

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