Expertise AnalysisSentiment Plus

Tom ReamyChief Knowledge Architect

KAPS Group

Knowledge Architecture Professional Services

http://www.kapsgroup.com

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

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

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

5

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

6

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

7

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

8

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

9

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

10

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

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

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

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

14

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

15

Education Terms

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

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

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

19

20

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

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

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?

Tom Reamytomr@kapsgroup.com

KAPS Group

Knowledge Architecture Professional Services

http://www.kapsgroup.com

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 Neuroscientist’s Quest for What Makes

Up Human – V. S. Ramachandran

25

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

26

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

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. 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

28

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

29

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

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

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