1. Expertise Analysis Sentiment Plus Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

2. 2 Agenda Introduction – Context – Sentiment Analysis – Second Generation – Categorization and Category Theory Basic Level Categories – Features and Issues Basic Level Categories and Expertise – Experts prefer lower levels – Categorization of Expertise Applications – Integration with Text Mining, Search, and ECM – Platform for Information Applications

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/Taxonomy development, consulting, customization – Technology Consulting – Search, CMS, Portals, etc. – Evaluation of Enterprise Search, Text Analytics – Metadata standards and implementation – Knowledge Management: Collaboration, Expertise, e-learning – Applied Theory – Faceted taxonomies, complexity theory, natural categories

4. 4 Introduction – Sentiment Analysis Sentiment & Categorization – Second Generation Emphasis on context around positive and negative words – Issue of sarcasm, slanguage – Really great product – Rules – not just statistical and terms Beyond Good and Evil (positive and negative) – Taxonomy of Objects and Features to taxonomy of emotions – Addition of focus on behaviors – why someone calls a support center – and likely outcomes Social Media Knowledge Base – Wisdom of crowds, crowd-sourcing

5. 5 Introduction – Sentiment Analysis Sentiment & Categorization Essential – need full categorization and concept extraction to do sentiment analysis well Sentiment Analysis to Expertise Analysis – Sentiment software plus cognitive science – Develop expertise categorization rules Categorization – Most basic to human cognition – Most difficult to do with software No single correct categorization – Women, Fire, and Dangerous Things

6. 6 Introduction – Sentiment Analysis Sentiment & Categorization Borges – Celestial Emporium of Benevolent Knowledge – Those that belong to the Emperor – Embalmed ones – Those that are trained – Suckling pigs – Mermaids – Fabulous ones – Stray dogs – Those that are included in this classification – Those that tremble as if they were mad – Innumerable ones – Other

7. 7 Basic Level Categories Introduction: What are Basic Level Categories? Mid-level in a taxonomy / hierarchy Levels: Superordinate – Basic – Subordinate – Mammal – Dog – Golden Retriever – Furniture – chair – kitchen chair Basic in 4 dimensions – Perception – overall perceived shape, single mental image, fast identification – Function – general motor program – Communication – shortest, most commonly used, neutral, first learned by children – Knowledge Organization – most attributes are stored at this level

8. 8 Basic Level Categories Introduction: Other levels Subordinate – more informative but less distinctive – Basic shape and function with additional details Ex – Chair – office chair, armchair – Convention – people name objects by their basic category label, unless extra information in subordinate is useful Superordinate – Less informative but more distinctive – All refer to varied collections – furniture – Often mass nouns, not count nouns – List abstract / functional properties – Very hard for children to learn

9. 9 Basic Level Categories Introduction: How recognize Basic level Short words – fewer noun phrases Kinds of attributes – Superordinate – functional (keeps you warm, sit on it) – Basic – Noun and adjectives – legs, belt loops, cloth – Subordinate – adjectives – blue, tall Basic Level – similar movements, similar shapes More complex for non-object domains Issue – what is basic level is context dependent

10. 10 Basic Level Categories Introduction: How recognize Basic level Cue Validity – probability that a particular object belongs to some category given that it has a particular feature (cue) – X has wings – bird – Superordinates have lower – fewer common attributes – Subordinates have lower – share more attributes with other members at same level Category utility – frequency of a category + category validity + base rates of each of these features Issue – how decide which features? – Cat – can be picked up, is bigger than a beetle

11. 11 Basic Level Categories and Expertise Experts prefer lower, subordinate levels – In their domain, (almost) never used superordinate – Novices prefer higher, superordinate levels – General Populace prefers basic level Not just individuals but whole societies / communities differ in their preferred levels Develop expertise rules – similar to categorization rules – Hybrid – all of the above – depending on context – Use basic level for subject – Superordinate for general, subordinate for expert

12. 12 Expertise Analysis: 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 Categorization rule – SENT, DIST – If same sentence, expert Demo – Sample Documents, Rules

13. 13 ExpertGeneral Research (context dependent)Kid StatisticalPay Program performanceClassroom ProtocolFail Adolescent AttitudesAttendance Key academic outcomesSchool year Job training programClosing American Educational Research AssociationCounselor Graduate management educationDiscipline Education Terms

14. 14 ExpertGeneral MouseCancer DoseScientific ToxicityPhysical DiagnosticConsumer MammographyCigarette SamplingSmoking InhibitorWeight gain EdemaCorrect NeoplasmsEmpirical IsotretinionDrinking EthyleneTesting SignificantlyLesson Population-baseKnowledge PharmacokineticMedicine MetaboliteSociology PolymorphismTheory SubsyndromicExperience RadionuclideServices EtiologyHospital OxidaseSocial CaptoprilDomestic Pharmacological agents Dermatotoxicity Mammary cancer model Biosynthesis Healthcare Terms

15. 15 Education Terms

16. 16 Expertise Analysis: Application areas Text Mining – Preprocessing of documents – Expertise characterization of writer, corpus – Best results with existing taxonomy (s) Can use a very general, high level taxonomy – superordinate and basic eCommerce – Organization and Presentation of information – expert, novice – How determine? Search queries, profiles, buying patterns, specific products

17. 17 Expertise Analysis: Application areas Search – enterprise and/or internet – Query level Relevance ranking – Adjust documents for novice and expert queries Information presentation – Tag clouds – match novice and expert Clustering – Incorporate into clustering algorithms Presentation – expose basic level & provide up and down browse

18. 18 Expertise Analysis: Application areas Social Media - Community of Practice – Characterize the level of expertise in the community – Evaluate other communities expertise level – Identify experts (and leaders) in the community Expertise location – Generate automatic expertise characterization based on authored documents Expertise of people in a social network – Terrorists and bomb-making

19. Expertise Analysis: Application areas - Tags Basic Level Blog Software (Design) Web (Design) Linux Javascript Web2.0 Google Css Flash Superordinate Music Photography News Education Business Technology Politics Science Culture 19

20. 20 Expertise Analysis: Application areas Business & Customer intelligence – General – characterize peoples expertise to add to evaluation of their comments Combine with VOC & 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 – Combine with categorization – offer tags that are suitable level of expertise

21. 21 Expertise Analysis: Future Directions Data mining + Text Mining + Expertise-Sentiment – New applications – Group Behavior – leaders, decisions Predictive Analytics – Adding new dimensions Neuro-Marketing, Economics, Law, Intelligence – Social forecasting – Twitter and Stock Market Language & category theory – Metaphor Analysis, etc. Need an emotion taxonomy?

22. 22 Expertise Analysis: Conclusions Expertise analysis adds a new dimension to Text Analysis and Sentiment Analysis – Broad range of applications – personalization, customer depth, Social Media, enterprise text analytics Expertise analysis builds on Basic Level Categories – Plus expertise categorization rules What is expert / basic level is context dependent Text & Expertise Analytics builds on Sentiment Analysis and Cognitive Science – Not just library science or data modeling or ontology or sentiment or linguistics – all of the above

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

24. 24 Resources Books – Affective Neuroscience: The Foundations of Human and Animal Emotions– Jaak Panskeep – Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics – Paul Glimcher – Women, Fire, and Dangerous Things George Lakoff – Knowledge, Concepts, and Categories Koen Lamberts and David Shanks – The Tell-Tale Brain: A Neuroscientists Quest for What Makes Up Human – V. S. Ramachandran

25. 25 Resources Web Sites – Text Analytics News - http://social.textanalyticsnews.com/index.php http://social.textanalyticsnews.com/index.php – Text Analytics Wiki - http://textanalytics.wikidot.com/http://textanalytics.wikidot.com/ – 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

26. 26 Resources Blogs – SAS- http://blogs.sas.com/text-mining/ http://blogs.sas.com/text-mining/ Web Sites – Whitepaper – CM and Text Analytics - http://www.textanalyticsnews.com/usa/contentmanagementm eetstextanalytics.pdf http://www.textanalyticsnews.com/usa/contentmanagementm eetstextanalytics.pdf – Whitepapers – Enterprise Content Categorization strategy and development – http://www.kapsgroup.comhttp://www.kapsgroup.com

27. 27 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. OConner 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

28. 28 Basic Level Categories Introduction: What are Basic Level Categories? Short and easy words Maximum distinctness and expressiveness Similarly perceived shapes Most commonly used labels Easiest and fastest to indentify members First level named and understood by children Terms usually used in neutral contexts Level at which most of our knowledge is organized Objects – most studied, most pronounced effects

29. 29 Basic Level Categories Introduction: Basic Level Categories: Non-Object Basic level effects, but no widespread acceptance of categories and category names Thus a basic level in a category hierarchy but not the category hierarchy that people actually use in everyday life Not just IS-A relationship – messier – more like ontologies Examples: – Scenes – indoors – school – elementary school – Events – travel – highway travel – truck travel – Emotions – positive emotion – joy – contentment – Programming – Algorithm – sort – binary

30. 30 Basic Level Categories and Expertise 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 – lower level terms only – Strategic – high level and lower level terms, special language

31. 31 Expertise Analysis: Techniques What is basic level is context(s) dependent Documents / Tags – analyze in terms of levels of words – Taxonomy for high level – Length for basic – short – Length for subordinate – long, special vocabulary Category Utility Develop expertise rules – similar to categorization rules – Hybrid – all of the above – depending on context – Use basic level for subject – Superordinate for general, subordinate for expert