Best of All Worlds Text Analytics and Text Mining and Taxonomy Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services

2 Agenda  Text Analytics Introduction – Text Analytics – Text Mining  Case Study – Taxonomy Development  Text Analytics, Text Mining, and Taxonomy,  Text Analytics Applications – New Directions – Search & Info Apps – Expertise Analysis, Behavior Prediction, More  Conclusions

3 KAPS Group: General  Knowledge Architecture Professional Services – Network of Consultants  Partners – SAS, SAP, IBM, FAST, Smart Logic, Concept Searching – Attensity, Clarabridge, Lexalytics,  Strategy – IM & KM - Text Analytics, Social Media, Integration  Services: – Taxonomy/Text Analytics development, consulting, customization – Text Analytics Quick Start – Audit, Evaluation, Pilot – Social Media: Text based applications – design & development  Clients: – Genentech, Novartis, Northwestern Mutual Life, Financial Times, Hyatt, Home Depot, Harvard Business Library, British Parliament, Battelle, Amdocs, FDA, GAO, etc.  Applied Theory – Faceted taxonomies, complexity theory, natural categories, emotion taxonomies Presentations, Articles, White Papers –

4 Taxonomy, Text Mining, 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

5 Taxonomy, Text Mining, 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

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Case Study – Categorization & Sentiment 7

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Taxonomy and Text Analytics 14

Taxonomy and Text Analytics 15

Taxonomy, Text Mining, 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 16

Case Study – Taxonomy Development 17

Case Study – Taxonomy Development 18

Case Study – Taxonomy Development 19

20 Text Analytics Development

21 New Directions in Social Media Text Analytics, Text Mining, and Predictive Analytics  Two Systems of the Brain – Fast, System 1, Immediate patterns (TM) – Slow, System 2, Conceptual, reasoning (TA)  Text Analytics – pre-processing for TM – Discover additional structure in unstructured text – Behavior Prediction – adding depth in individual documents – New variables for Predictive Analytics, Social Media Analytics – New dimensions – 90% of information  Text Mining for TA– Semi-automated taxonomy development – Bottom Up- terms in documents – frequency, date, clustering – Improve speed and quality – semi-automatic

22 Text Analytics and Taxonomy Complimentary Information Platform  Taxonomy provides a consistent and common vocabulary – Enterprise resource – integrated not centralized  Text Analytics provides a consistent tagging – Human indexing is subject to inter and intra individual variation  Taxonomy provides the basic structure for categorization – And candidates terms  Text Analytics provides the power to apply the taxonomy – And metadata of all kinds  Text Analytics and Taxonomy Together – Platform – Consistent in every dimension – Powerful and economic

23 Taxonomy, Text Mining, and Text Analytics Metadata – Tagging – the Problem  How do you bridge the gap – taxonomy to documents?  Tagging documents with taxonomy nodes is tough – And expensive – central or distributed  Library staff –experts in categorization not subject matter – Too limited, narrow bottleneck – Often don’t understand business processes and business uses  Authors – Experts in the subject matter, terrible at categorization – Intra and Inter inconsistency, “intertwingleness” – Choosing tags from taxonomy – complex task – Folksonomy – almost as complex, wildly inconsistent – Resistance – not their job, cognitively difficult = non-compliance  Text Analytics is the answer(s)!

24 Taxonomy, Text Mining, and Text Analytics Metadata Tagging – the Solution  Mind the Gap – Manual, Automatic, Hybrid  All require human effort – issue of where and how effective  Manual - human effort is tagging (difficult, inconsistent)  Automatic and Hybrid - human effort is prior to tagging – Build on expertise – librarians on categorization, SME’s on subject terms  Hybrid Model – Publish Document -> Text Analytics analysis -> suggestions for categorization, entities, metadata - > present to author – Cognitive task is simple -> react to a suggestion instead of select from head or a complex taxonomy – Feedback – if author overrides -> suggestion for new category – Facets – Requires a lot of Metadata - Entity Extraction feeds facets  Hybrid – Automatic is really a spectrum – depends on context

25 Taxonomy, Text Mining, and Text Analytics Applications: Search  Multiple Knowledge Structures – Facet – orthogonal dimension of metadata – Taxonomy - Subject matter / aboutness – Ontology – Relationships / Facts Subject – Verb - Object  Software - Search, ECM, auto-categorization, entity extraction, Text Analytics and Text Mining  People – tagging, evaluating tags, fine tune rules and taxonomy  People – Users, social tagging, suggestions  Rich Search Results – context and conversation

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28 Taxonomy, Text Mining, and Text Analytics Applications: Search-Based 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!

29 Taxonomy, Text Mining, 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

30 Taxonomy, Text Mining, and Text Analytics 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

31 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

32 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

33 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

34 Taxonomy, Text Mining, and Text Analytics Conclusions  Text Analytics is an essential platform for multiple applications  Text Analytics and Text Mining and Taxonomy are mutually enriching approaches  Sentiment Analysis, Beyond Positive & Negative  New emotion taxonomies, context around terms  New applications – Expertise, behavior prediction, etc.  Future – new kinds of applications: – Enterprise Search – Hybrid ECM model with text analytics – Expertise Analysis, Behavior Prediction, and more – Social Media and Big Data built from TM & TA – NeuroAnalytics – cognitive science meets taxonomy and more Watson is just the start

Questions? Tom Reamy KAPS Group Knowledge Architecture Professional Services

36 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

37 Resources  Conferences – Web Sites – Text Analytics World – – Text Analytics Summit – – Semtech –

38 Resources  Blogs – SAS-  LinkedIn Groups: – Text Analytics World – Text Analytics Group – Data and Text Professionals – Sentiment Analysis – Metadata Management – Semantic Technologies

39 Resources  Web Sites – Taxonomy Community of Practice: – Whitepaper – CM and Text Analytics - eetstextanalytics.pdf eetstextanalytics.pdf – Whitepaper – Enterprise Content Categorization strategy and development –

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