1. Introduction to Text Analytics
October 2, 2013
Dr. Stuart Shulman
Phone No.: +1-413-345-8939
E-mail: stu.shulman@visioncritical.com

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. How Do These Tools Help Analysts?

4. What This Means forAnalysis

5. The Core Methods
Coding and Classifying Text Data

6. Iteration and Re-Use Are Critical Techniques

7. Measure Everything Starting With Human Agreement

8. The Core DiscoverTextApproach

9. An Indispensable Role for Humans

10. Innovation Happens in Groups

11. “CoderRank” – A LifetimeAccuracy Measurement
Vision Critical Patent Pending – “Enhanced Machine Learning”

12. Five Essential Tools for TextAnalytics
1. Search
2. Filtering on Metadata
3. Human Coding
4. Automated Clustering
5. Machine Classification

13. A Social Media Use Case
Sifting and Sorting Relevant Data

14. Great Researchers Demand Transparent Tools

15. The HMC is a Leading Edge Gnip Customer

16. Gnip Data Streams and Search Filters

17. Fair Warning
This part of the presentation contains strong and
potentially quite
offensive, inappropriate, disturbing, or just
completely stupid language.

18. Studying Media Campaign Effects

19. Create Custom Machine Classifiers
Yes
No
No

20. Search is Fundamental for Purposive Sampling

21. Defined Search Speeds Up Discovery

22. Tumblr. – “The Wild West of the Internet”

23. Stupid Stuff People Do & Tweet
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24. Are These Tweets Just Social Garbage?
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25. Signs of Health Fear Engagement
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26. An IdeaScreen Use Case
Concept Testing Data

27. Raw VoC Data: AFortune 500 Tech Company

28. Near Duplicate Clusters Can Be Interesting

29. Two Naturally Occurring Clusters of Free Text

30. Wherever Humans Go in Numbers, There Are Clusters

31. 1st Wave of Human Coding Blazes a Trail

32. A„Simple‟ Coding Scheme with No Coder Training

33. Filtering Based on Classifier Scores

34. Testing Coder Agreement on a Small Sample

35. Measuring Inter-CoderAgreement

36. Validation of Coders & Codes

37. TextAnalytics is a Series Buckets & Datasets

38. Breaking Down Concerns by Subtype

39. Breaking Down Advocacy by Pro and Con

40. A New Vision Critical Front End
The First Preview of the New Release

41. The New VC Front End for DiscoverText

42. Coding Items to Train a Classifier

43. Leverage Item Metadata While Coding or Filtering

44. Code Items in a List View