1. Taxonomy Boot Camp Panel Text Analytics Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

2. 2 Agenda  Taxonomy and Text Analytics – Search, Taxonomy, and Text Analytics  Case Study – Taxonomy Development – Text Analytics as a Taxonomy tool – Case Studies – Expertise & Sentiment & Beyond  Future of Text Analytics and Taxonomy – Beyond Indexing - Categorization – Sentiment, Expertise, Ontologies

3. 3 Taxonomy and Text Analytics Text Analytics Features  Noun Phrase Extraction – Catalogs with variants, rule based dynamic – Multiple types, custom classes – entities, concepts, events – Feeds facets  Summarization – Customizable rules, map to different content  Fact Extraction – Relationships of entities – people-organizations-activities – Ontologies – triples, RDF, etc.  Sentiment Analysis – Rules – Objects and phrases – positive and negative

4. 4 Taxonomy and Text Analytics Text Analytics Features  Auto-categorization – Training sets – Bayesian, Vector space – Terms – literal strings, stemming, dictionary of related terms – Rules – simple – position in text (Title, body, url) – Semantic Network – Predefined relationships, sets of rules – Boolean– Full search syntax – AND, OR, NOT – Advanced – DIST (#), PARAGRAPH, SENTENCE  This is the most difficult to develop  Build on a Taxonomy  Combine with Extraction – If any of list of entities and other words

5. Case Study – Categorization & Sentiment 5

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7. 7 Search, Taxonomy, and Text Analytics Elements  Multiple Knowledge Structures – Facet – orthogonal dimension of metadata – Taxonomy - Subject matter / aboutness – Categorization, clusters, entity extraction into facets  A Hybrid Model of ECM and Metadata – Authors, editors-librarians, Text Analytics – Submit a document -> TA generates metadata, extracts concepts, Suggests categorization (keywords) -> author OK’s (easy task) -> librarian monitors for issues – Use results as input into analytics  And/or Dynamic categorization-extraction at results time

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10. 10 Search, Taxonomy and Text Analytics Multiple Applications  Platform for Information Applications – Content Aggregation – Duplicate Documents – save millions! – Text Mining – BI, CI – sentiment analysis – Combine with Data Mining – disease symptoms, new Predictive Analytics – Social – Hybrid folksonomy / taxonomy / auto-metadata – Social – expertise, categorize tweets and blogs, reputation – Ontology – travel assistant – SIRI  Use your Imagination!

11. Taxonomy and Text Analytics Case Study – Taxonomy Development  Problem – 200,000 new uncategorized documents  Old taxonomy –need one that reflects change in corpus  Text mining, entity extraction, categorization  Content – 250,000 large documents, search logs, etc.  Bottom Up- terms in documents – frequency, date,  Clustering – suggested categories  Clustering – chunking for editors  Entity Extraction – people, organizations, Programming languages  Time savings – only feasible way to scan documents  Quality – important terms, co-occurring terms 11

12. Case Study – Taxonomy Development 12

13. Case Study – Taxonomy Development 13

14. Case Study – Taxonomy Development 14

15. 15 Taxonomy and Text Analytics Applications Expertise Analysis  Sentiment Analysis to Expertise Analysis(KnowHow) – Know How, skills, “tacit” knowledge  Experts write and think differently  Basic level is lower, more specific – Levels: Superordinate – Basic – Subordinate Mammal – Dog – Golden Retriever – Furniture – chair – kitchen chair  Experts organize information around processes, not subjects  Build expertise categorization rules

16. 16 Expertise Analysis Expertise – application areas  Taxonomy / Ontology development /design – audience focus – Card sorting – non-experts use superficial similarities  Business & Customer intelligence – add expertise to sentiment – Deeper research into communities, customer s  Text Mining - Expertise characterization of writer, corpus  eCommerce – Organization/Presentation of information – expert, novice  Expertise location- Generate automatic expertise characterization based on documents  Experiments - Pronoun Analysis – personality types – Essay Evaluation Software - Apply to expertise characterization Model levels of chunking, procedure words over content

17. 17 Beyond Sentiment: Behavior Prediction Case Study – Telecom 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

18. 18 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 and Predictive Analytics

19. 19 Beyond Sentiment - Wisdom of Crowds Crowd Sourcing Technical Support  Example – Android User Forum  Develop a taxonomy of products, features, problem areas  Develop Categorization Rules: – “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.” – 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

20. 20 Text Analytics Development Best Practices - Principles  Categorization taxonomy structure – Tradeoff of depth and complexity of rules – Multiple avenues – facets, terms, rules, etc. No right balance – Recall-precision balance is application specific – Training sets of starting points, rules rule – Need for custom development  Different kinds of taxonomies – Sentiment – products and features – Expertise – process – Categorization – smaller – power in categorization rules – Facets – combine – more orthogonal categories

21. 21 Taxonomy and Text Analytics Conclusions  Text Analytics (Entity extraction and auto-categorization, sentiment analysis) are an essential platform  Text Analytics add a new dimension to taxonomy – Taxonomists are an essential resource – understand information structure  Enterprise Search – Hybrid ECM model with text analytics  Future – new kinds of applications: – Text Mining and Data mining, research tools, sentiment – Social Media – multiple sources for multiple applications – Beyond Sentiment – expertise applications, behavior – NeuroAnalytics – cognitive science meets taxonomy and more Watson is just the start

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