Summit slide loop ny

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Summit slide loop ny

  1. 1. Introduction to Text Analytics October 2, 2013 Dr. Stuart Shulman Phone No.: +1-413-345-8939 E-mail: stu.shulman@visioncritical.com
  2. 2. The Value Proposition Our solution helps users easily discover information to: • streamline business processes • increase ROI & create new business opportunities • identify positive and negative trends • discover unique, rare or unexpected information
  3. 3. How Do These Tools Help Analysts?
  4. 4. What This Means forAnalysis
  5. 5. The Core Methods Coding and Classifying Text Data
  6. 6. Iteration and Re-Use Are Critical Techniques
  7. 7. Measure Everything Starting With Human Agreement
  8. 8. The Core DiscoverTextApproach
  9. 9. An Indispensable Role for Humans
  10. 10. Innovation Happens in Groups
  11. 11. “CoderRank” – A LifetimeAccuracy Measurement Vision Critical Patent Pending – “Enhanced Machine Learning”
  12. 12. Five Essential Tools for TextAnalytics 1. Search 2. Filtering on Metadata 3. Human Coding 4. Automated Clustering 5. Machine Classification
  13. 13. A Social Media Use Case Sifting and Sorting Relevant Data
  14. 14. Great Researchers Demand Transparent Tools
  15. 15. The HMC is a Leading Edge Gnip Customer
  16. 16. Gnip Data Streams and Search Filters
  17. 17. Fair Warning This part of the presentation contains strong and potentially quite offensive, inappropriate, disturbing, or just completely stupid language.
  18. 18. Studying Media Campaign Effects
  19. 19. Create Custom Machine Classifiers Yes No No
  20. 20. Search is Fundamental for Purposive Sampling
  21. 21. Defined Search Speeds Up Discovery
  22. 22. Tumblr. – “The Wild West of the Internet”
  23. 23. Stupid Stuff People Do & Tweet redacted redacted
  24. 24. Are These Tweets Just Social Garbage? redacted redacted
  25. 25. Signs of Health Fear Engagement redacted redacted
  26. 26. An IdeaScreen Use Case Concept Testing Data
  27. 27. Raw VoC Data: AFortune 500 Tech Company
  28. 28. Near Duplicate Clusters Can Be Interesting
  29. 29. Two Naturally Occurring Clusters of Free Text
  30. 30. Wherever Humans Go in Numbers, There Are Clusters
  31. 31. 1st Wave of Human Coding Blazes a Trail
  32. 32. A„Simple‟ Coding Scheme with No Coder Training
  33. 33. Filtering Based on Classifier Scores
  34. 34. Testing Coder Agreement on a Small Sample
  35. 35. Measuring Inter-CoderAgreement
  36. 36. Validation of Coders & Codes
  37. 37. TextAnalytics is a Series Buckets & Datasets
  38. 38. Breaking Down Concerns by Subtype
  39. 39. Breaking Down Advocacy by Pro and Con
  40. 40. A New Vision Critical Front End The First Preview of the New Release
  41. 41. The New VC Front End for DiscoverText
  42. 42. Coding Items to Train a Classifier
  43. 43. Leverage Item Metadata While Coding or Filtering
  44. 44. Code Items in a List View

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