1. Beyond Sentiment Mining Social Media Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

2. 2 Agenda  Introduction – Text Analytics & Sentiment Analysis  Expertise Analysis – Basic Level Categories – Categorization of Expertise  Social Behavior Predictions – Distinguishing Action from Expression  Social Media – Wisdom of Crowds – Cloud Sourcing technical support  Questions

3. 3 KAPS Group: General  Knowledge Architecture Professional Services  Virtual Company: Network of consultants – 8-10  Partners – SAS, Smart Logic, Microsoft-FAST, Concept Searching, etc.  Consulting, Strategy, Knowledge architecture audit  Services: – Text Analytics evaluation, development, consulting, customization – Knowledge Representation – taxonomy, ontology, Prototype – Metadata standards and implementation – Knowledge Management: Collaboration, Expertise, e-learning – Applied Theory – Faceted taxonomies, complexity theory, natural categories

4. 4 Introduction to Text Analytics Text Analytics Features  Text Extraction (Noun phrase, themes, parts of speech) – Catalogs with variants, rule based dynamic – Multiple types, custom classes – entities, concepts, events  Fact Extraction – Relationships of entities – people-organizations-activities – Ontologies – triples, RDF, etc. // Disambiguation  Auto-categorization – Build on a Taxonomy – Training sets – Bayesian, Vector space – Boolean– Full search syntax – AND, OR, NOT, DIST#, SENT  This is the most difficult to develop  Foundation for all applications

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6. Case Study – Categorization & Sentiment 6

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8. Text Analytics and Text Mining Data and Unstructured Content  80% of content is unstructured – adding to semantic web is major  Text Analytics – content into data – Big Data meets Big Content  Real integration of text and ontology – Beyond “hasDescription” – Improve accuracy of extracted entities, facts – disambiguation Pipeline – oil & gas OR research / Ford – Add Concepts, not just “Things” – 68% want this  Semantic Web + Text Analytics = real world value  Linked Data + Text Analytics – best of both worlds  Build superior foundation elements – taxonomies, categorization 8

9. 9 Sentiment Analysis Development Process  Combination of Statistical and categorization rules  Start with Training sets – examples of positive, negative, neutral documents (find good examples – forums, etc.)  Develop a Statistical Model  Generate domain positive and negative words and phrases  Develop a taxonomy of Products & Features  Develop rules for positive and negative statements  Test and Refine  Test and Refine again

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15. 15 Expertise Analysis Basic Level Categories  Levels: Superordinate – Basic – Subordinate – Mammal – Dog – Golden Retriever – Furniture – chair – kitchen chair  Mid-level in a taxonomy / hierarchy  Short and easy words, similarly perceived shapes  Maximum distinctness and expressiveness  Most commonly used labels  First level named and understood by children  Level at which most of our knowledge is organized

16. 16 Basic Level Categories and Expertise  Experts prefer lower, subordinate levels – Novice prefer higher, superordinate levels – General Populace prefers basic level  Expertise Characterization for individuals, communities, documents, and sets of documents  Experts chunk series of actions, ideas, etc. – Novice – high level only – Intermediate – steps in the series – Expert – special language – based on deep connections  Types of expert – technical, strategic

17. 17 Expertise Analysis Analytical Techniques  Corpus context dependent – Author748 – is general in scientific health care context, advanced in news health care context  Need to generate overall expertise level for a corpus  Also contextual rules – “Tests” is general, high level – “Predictive value of tests” is lower, more expert  Develop expertise rules – similar to categorization rules – Use basic level for subject – Superordinate for general, subordinate for expert

18. 18 Expertise Analysis Application areas  Business & Customer intelligence / Social Media – Combine with sentiment analysis – finer evaluation – what are experts saying, what are novices saying – Deeper research into communities, customers  Enterprise Content Management – At publish time, software automatically gives an expertise level – present to author for validation  Expertise location – Generate automatic expertise characterization based on authored documents

19. 19 Beyond Sentiment Behavior Prediction – Case Study  Telecommunications Customer Service  Problem – distinguish customers likely to cancel from mere threats  Analyze customer support notes  General issues – creative spelling, second hand reports  Develop categorization rules – First – distinguish cancellation calls – not simple – Second - distinguish cancel what – one line or all – Third – distinguish real threats

20. 20 Beyond Sentiment Behavior Prediction – Case Study  Basic Rule – (START_20, (AND, – (DIST_7,"[cancel]", "[cancel-what-cust]"), – (NOT,(DIST_10, "[cancel]", (OR, "[one-line]", "[restore]", “[if]”)))))  Examples: – customer called to say he will cancell his account if the does not stop receiving a call from the ad agency. – cci and is upset that he has the asl charge and wants it off or her is going to cancel his act – ask about the contract expiration date as she wanted to cxl teh acct Combine sophisticated rules with sentiment statistical training

21. 21 Beyond Sentiment - Wisdom of Crowds Cloud / Crowd Sourcing Technical Support  Example – Android User Forum  Develop a taxonomy of products, features, problem areas  Develop Categorization Rules: – Find product & feature – forum structure – Find problem areas in response – Nearby Text for solution  Automatic – simply expose lists of “solutions” – Search Based application  Human mediated – experts scan and clean up solutions

22. 22 Beyond Sentiment - Wisdom of Crowds Cloud / Crowd Sourcing Technical Support  Quote:  Originally Posted by jersey221  you either need to be rooted and download a screenshot app from the market like picme,shootme.or download the android sdk and use that..im not quite sure about the sdk method.  I use the SDK method and it isn't to bad a all. I'll get some pics up later, I am still trying to get the time to update from fresh 1.0 to 1.1.  Device(s): Fresh 2.1.1  Thanks: 36  Thanked 37 Times in 26 Posts

23. 23 Beyond Sentiment - Wisdom of Crowds Cloud / Crowd Sourcing Technical Support  Quote: Originally Posted by jersey221  its not on the marketplace its called taps of fire  here's a download for it when you download it put it on your sd card then look for it on a file manager like es file explorer  or astro on you phone then click it and open in manager or something like that and then install it and you should be good.  TapsOfFire104.apk - tapsoffire - Taps Of Fire (1.0.4) - Project Hosting on Google Code  i am guessing my phone needs to be rooted for something like this to happen.  Device(s): rooted htc hero with fresh 1.1 rom  Thanks: 21 - Thanked 3 Times in 3 Posts

24. 24 Beyond Sentiment Conclusions  Text Analytics turns text into data – semantic web, predictive analytics  Sentiment Analysis needs good categorization  Expertise Analysis can add a new dimension to sentiment – More sophisticated Voice of the Customer  Multiple Applications from Expertise analysis – search, BI, CI, Enterprise Content Management, Expertise Location  New Directions – Behavior Prediction, Crowd Sourcing, ?  Text Analytics needs Cognitive Science – Not just library science or data modeling or ontology

25. Questions? Tom Reamy tomr@kapsgroup.com KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

26. 26 Resources  Books – Women, Fire, and Dangerous Things George Lakoff – Knowledge, Concepts, and Categories Koen Lamberts and David Shanks – Formal Approaches in Categorization Ed. Emmanuel Pothos and Andy Wills – The Mind Ed John Brockman Good introduction to a variety of cognitive science theories, issues, and new ideas – Any cognitive science book written after 2009

27. 27 Resources  Conferences – Web Sites – Text Analytics World – http://www.textanalyticsworld.com http://www.textanalyticsworld.com – Text Analytics Summit – http://www.textanalyticsnews.com http://www.textanalyticsnews.com – Semtech – http://www.semanticweb.com http://www.semanticweb.com

28. 28 Resources  Blogs – SAS- http://blogs.sas.com/text-mining/ http://blogs.sas.com/text-mining/  Web Sites – Taxonomy Community of Practice: http://finance.groups.yahoo.com/group/TaxoCoP/ http://finance.groups.yahoo.com/group/TaxoCoP/ – LindedIn – Text Analytics Summit Group – http://www.LinkedIn.com http://www.LinkedIn.com – Whitepaper – CM and Text Analytics - http://www.textanalyticsnews.com/usa/contentmanagementm eetstextanalytics.pdf http://www.textanalyticsnews.com/usa/contentmanagementm eetstextanalytics.pdf – Whitepaper – Enterprise Content Categorization strategy and development – http://www.kapsgroup.comhttp://www.kapsgroup.com

29. 29 Resources  Articles – Malt, B. C. 1995. Category coherence in cross-cultural perspective. Cognitive Psychology 29, 85-148 – Rifkin, A. 1985. Evidence for a basic level in event taxonomies. Memory & Cognition 13, 538-56 – Shaver, P., J. Schwarz, D. Kirson, D. O’Conner 1987. Emotion Knowledge: further explorations of prototype approach. Journal of Personality and Social Psychology 52, 1061-1086 – Tanaka, J. W. & M. E. Taylor 1991. Object categories and expertise: is the basic level in the eye of the beholder? Cognitive Psychology 23, 457-82