Sentiment Analysis Symposium Sentiment & Triangulation © Anderson Analytics LLC. All Rights Reserved
Why Now: Difference in Technology <ul><li>Different AI Levels of Understanding Text Data </li></ul>Machine more accurate t...
Advantage Machine Coding Human Coding Diagram Copyright ©  Anderson Analytics, LLC iterative Difficulty in modifying code ...
AA Text Mining vs Qualitative Qualitative Identified Concepts Text Mining Identified Concepts Universe of text data in a s...
Validation Through Triangulation Data Mining/Visualization Neural Nets, Factoring, Clustering, Logistic Regression… I. Qua...
An Unexplored Opportunity: Listening to the “The Voice of a Million Customers”  Copyright 2005, SPSS Inc. <ul><li>About 75...
Listening to the “The Voice of a Million Customers”   “… Check-In” “ Good…” “ Not Clean…” “… Not Working” “ Disappointed…”...
About Your Customers <ul><li>Visualizing Data (100+posts/user) </li></ul><ul><ul><li>Data flows like a river, Data has sha...
Value to Starwood Hotels and Hospitality Industry <ul><li>Starwood Hotels and Resorts was delighted participate in this te...
The Future…
LinkedIn <ul><li>2008 Linked In Study </li></ul><ul><li>LinkedIn Database vs. Profile Text vs. Member Survey </li></ul><ul...
Most Used Terms in User Headlines  <ul><li>Teacher makes $46K Spending Power $1.6K </li></ul><ul><li>Student makes $30K Sp...
<ul><li>First Sample n=53,873 records. </li></ul><ul><li>Original Seed (1,000 US + 1,000 ROW) + First Level Connections (A...
Visualizing Social Networks On LinkedIn Seed A (22 Connections) Connection C Seed B (213 Connections) 1 st  Level Connecti...
The Future
*Variables NOT used in clustering LinkedIn Segments – Important Variables (Neural Net) LI Interests/Purpose Work Situation...
The Future - SNS
The Future - SNS Source: Anderson Analytics April 2009
Thank You! + 1-888-891-3115 email: Inquiries@andersonanalytics.com twitter: @TomHCAnderson blog: www.tomhcanderson.com
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Voice of the Market, Tom Anderson

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Text analytics can be leveraged in many areas of market research. Tom gives real case study examples of how his firm has merged text analytics with traditional market research and helped fortune 500 clients with customer satisfaction, competitive intelligence, and segmentation.

He discusses techniques that provide validation through triangulation. Going beyond verbatim concept, themes and negative/positive/neutral sentiment, Anderson Analytics also leverages psychological content analysis which utilize a priori word choice models and compares these to normative, category and demographic specific databases.

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  • SPSS Inc. Copyright 2004, SPSS Inc.
  • SPSS Inc. Copyright 2004, SPSS Inc.
  • Voice of the Market, Tom Anderson

    1. 1. Sentiment Analysis Symposium Sentiment & Triangulation © Anderson Analytics LLC. All Rights Reserved
    2. 2. Why Now: Difference in Technology <ul><li>Different AI Levels of Understanding Text Data </li></ul>Machine more accurate than human Human more accurate than machine Past Present Level 1 2 3 4 5 Key function Word Count (including inflected forms) Grouping of synonyms Word Association Grouping of related terms Detecting Positive/ negative sentiment Meaning in context Implication Output example Bed=2 Room=5 Wine=6 Great= (fantastic, excellent, wonderful) Dirty=(filthy, smelly, dirty) Furniture=(chair, table, couch) Food=(bread, shrimp Furniture+<positive> Food+<negative> People talking about their dining experience People talk about how the dining experience relate to their overall vacation experience Accuracy
    3. 3. Advantage Machine Coding Human Coding Diagram Copyright © Anderson Analytics, LLC iterative Difficulty in modifying code book Inter-coder reliability issues Similar surveys can be coded easily Text data
    4. 4. AA Text Mining vs Qualitative Qualitative Identified Concepts Text Mining Identified Concepts Universe of text data in a study Extreme Outliers Qualitative analysis only accounts for a small sample of the available data set. Concept proportionality, importance and relevance can get distorted. Extreme outliers might be overlooked. Text mining accounts for most of the data. Extraction of concepts and categorization of data are more accurate. Extreme outliners can be identified.
    5. 5. Validation Through Triangulation Data Mining/Visualization Neural Nets, Factoring, Clustering, Logistic Regression… I. Quantitative Triangulated Validation III. Qualitative II. Psychological Text Mining (non a priori) Random Sample (a priori) Review/Confirmation Psychological Measures Review/Confirmation Verbatim Concepts and Themes
    6. 6. An Unexplored Opportunity: Listening to the “The Voice of a Million Customers” Copyright 2005, SPSS Inc. <ul><li>About 750 properties; 300,000 rooms; 82 countries </li></ul><ul><li>6 Major Brands </li></ul><ul><li>1 Million Surveys Analyzed each year </li></ul><ul><li>Current Database 5+ million records </li></ul>“ Good…” “ Service…” “… Bad…” “… Bathroom…” “ Bed…” “ Not Clean…” “… Reservation” “… Not Working” “ Disappointed…” “… Management” “… Check-In” “… Charge” “ Excellent…” “ Loud…” “ Not Acceptable…” “… Not Friendly”
    7. 7. Listening to the “The Voice of a Million Customers” “… Check-In” “ Good…” “ Not Clean…” “… Not Working” “ Disappointed…” “ Excellent…” “ Loud…” “… Not Friendly” “… Management” “… Charge” *For Example Only/Concepts Disguised
    8. 8. About Your Customers <ul><li>Visualizing Data (100+posts/user) </li></ul><ul><ul><li>Data flows like a river, Data has shape </li></ul></ul><ul><ul><li>Network Chart </li></ul></ul>
    9. 9. Value to Starwood Hotels and Hospitality Industry <ul><li>Starwood Hotels and Resorts was delighted participate in this text mining project. Understanding the key words that drive verbal satisfaction could provide another important tool for General Managers to ensure that a guest's stay is a great one. Being better able to judge how satisfied a guest is while they are still at the hotel provides another opportunity to make the guest's experience a positive one, which is the most important factor in the decision to return to the hotel and ultimately to drive true preference for our brands. </li></ul>Rebecca Gillan VP, Global Market Research and Guest Satisfaction Starwood Hotels and Resorts Worldwide, Inc.
    10. 10. The Future…
    11. 11. LinkedIn <ul><li>2008 Linked In Study </li></ul><ul><li>LinkedIn Database vs. Profile Text vs. Member Survey </li></ul><ul><li>Sampling: </li></ul><ul><ul><li>Panelists vs. SNS Members Lower Income AND Lower Seniority </li></ul></ul><ul><ul><li>However, willing to take relevant studies through network </li></ul></ul><ul><li>Text Mining </li></ul><ul><ul><li>Able to Predict Income AND Purchasing Power </li></ul></ul><ul><ul><li>Predict, keep short, ask fewer questions </li></ul></ul><ul><li>“ Headline” </li></ul><ul><li>Title </li></ul><ul><li>Schools </li></ul><ul><li>Companies </li></ul><ul><li>Connections </li></ul><ul><li>… . </li></ul>Text Mine (Sample & Predict)
    12. 12. Most Used Terms in User Headlines <ul><li>Teacher makes $46K Spending Power $1.6K </li></ul><ul><li>Student makes $30K Spending power $4K </li></ul>(and their monetary value) Income Purchase Power Title Rank Mean Rank Mean vp 1 $190,000 3 $200,250 advertising 2 $187,500 4 $175,000 contractor 3 $150,000 5 $154,375 chief__officers 4 $145,455 1 $252,262 partner 5 $126,429 25 $54,500 executive 6 $121,094 15 $99,444 owner 7 $118,625 21 $73,698 sales 8 $118,000 24 $57,759 marketing 9 $116,667 12 $105,375 consultant 10 $116,486 29 $40,227 director 11 $115,330 6 $137,712 financial 12 $113,636 14 $99,900 senior 13 $111,116 17 $89,515 operations 14 $103,125 18 $88,750 technology 15 $99,286 8 $127,500 manager 16 $99,042 11 $108,601 computer 17 $97,500 34 $13,750 engineer 18 $92,857 27 $49,528 software 19 $91,912 32 $28,646 services 20 $88,226 23 $58,882 information 21 $87,500 2 $212,500 associates 22 $87,083 16 $95,429 human resources 23 $85,833 22 $61,042 analyst 24 $83,594 20 $81,447 development 25 $83,462 33 $15,735 professional 26 $78,421 26 $53,301 assistant 27 $77,344 9 $116,406 account 28 $77,206 28 $42,105 program 29 $70,833 7 $128,056 medical 30 $66,667 13 $104,444 attorney 31 $66,250 30 $36,250 real_estate 32 $65,625 19 $84,722 designer 33 $65,625 31 $30,417 health 34 $63,824 10 $114,211 teacher 35 $45,833 36 $1,667 student 36 $30,441 35 $3,977
    13. 13. <ul><li>First Sample n=53,873 records. </li></ul><ul><li>Original Seed (1,000 US + 1,000 ROW) + First Level Connections (Approx. 30 Connections Per Seed) </li></ul><ul><li>Zooming in to explore micro level networks on LinkedIn using Clementine Web Charting (n=5,000) </li></ul>Visualizing Social Networks On LinkedIn 1 st Level Connection Original Seed
    14. 14. Visualizing Social Networks On LinkedIn Seed A (22 Connections) Connection C Seed B (213 Connections) 1 st Level Connection Original Seed
    15. 15. The Future
    16. 16.
    17. 17. *Variables NOT used in clustering LinkedIn Segments – Important Variables (Neural Net) LI Interests/Purpose Work Situation* Purchase Behavior* Use of LinkedIn
    18. 18. The Future - SNS
    19. 19. The Future - SNS Source: Anderson Analytics April 2009
    20. 20. Thank You! + 1-888-891-3115 email: Inquiries@andersonanalytics.com twitter: @TomHCAnderson blog: www.tomhcanderson.com

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