expertise analysis sentiment plus tom reamy chief knowledge architect kaps group knowledge...
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Expertise AnalysisSentiment Plus
Tom ReamyChief Knowledge Architect
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
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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
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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
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Introduction – Sentiment AnalysisSentiment & 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
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Introduction – Sentiment AnalysisSentiment & 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
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Introduction – Sentiment AnalysisSentiment & 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
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Basic Level CategoriesIntroduction: 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
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Basic Level CategoriesIntroduction: 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
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Basic Level CategoriesIntroduction: 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
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Basic Level CategoriesIntroduction: 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
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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
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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
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Expert General
Research (context dependent) Kid
Statistical Pay
Program performance Classroom
Protocol Fail
Adolescent Attitudes Attendance
Key academic outcomes School year
Job training program Closing
American Educational Research Association Counselor
Graduate management education Discipline
Education Terms
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Expert General
Mouse Cancer
Dose Scientific
Toxicity Physical
Diagnostic Consumer
Mammography Cigarette
Sampling Smoking
Inhibitor Weight gain
Edema Correct
Neoplasms Empirical
Isotretinion Drinking
Ethylene Testing
Significantly Lesson
Population-base Knowledge
Pharmacokinetic Medicine
Metabolite Sociology
Polymorphism Theory
Subsyndromic Experience
Radionuclide Services
Etiology Hospital
Oxidase Social
Captopril Domestic
Pharmacological agents
Dermatotoxicity
Mammary cancer model
Biosynthesis
Healthcare Terms
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Education Terms
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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
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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
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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
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
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Expertise Analysis: Application areas
Business & Customer intelligence– General – characterize people’s 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
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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?
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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
Questions?
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
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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 Neuroscientist’s Quest for What Makes
Up Human – V. S. Ramachandran
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Resources
Web Sites– Text Analytics News -
http://social.textanalyticsnews.com/index.php
– Text Analytics Wiki - http://textanalytics.wikidot.com/– Taxonomy Community of Practice:
http://finance.groups.yahoo.com/group/TaxoCoP/– LindedIn – Text Analytics Summit Group– http://www.LinkedIn.com
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Resources
Blogs– SAS- http://blogs.sas.com/text-mining/
Web Sites – Whitepaper – CM and Text Analytics -
http://www.textanalyticsnews.com/usa/contentmanagementmeetstextanalytics.pdf
– Whitepapers – Enterprise Content Categorization strategy and development – http://www.kapsgroup.com
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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
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Basic Level CategoriesIntroduction: 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
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Basic Level CategoriesIntroduction: 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
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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
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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