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Text Analytics World
Current Applications and
Future Directions of Text Analytics
Tom Reamy
Chief Knowledge Architect
KAPS Group
Program Chair – Text Analytics World
Knowledge Architecture Professional Services
http://www.kapsgroup.com
Agenda
 Introduction:
– Current State of Text Analytics
– Survey / Discussion Themes
 Enterprise Text Analytics - Search – still fundamental
– Shift from information to business
 Social Media – Next Generation
– Text Analytics and CRM
 Integration – Text and Data, Enterprise and Social
 Future of Text Analytics
– Roadblocks, Deep Vision
 Questions
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Introduction: KAPS Group
 Knowledge Architecture Professional Services – Network of Consultants
 Applied Theory – Faceted taxonomies, complexity theory, natural
categories, emotion taxonomies
 Services:
– Strategy – IM & KM - Text Analytics, Social Media, Integration
– Taxonomy/Text Analytics development, consulting, customization
– Text Analytics Quick Start – Audit, Evaluation, Pilot
– Social Media: Text based applications – design & development
 Partners – SAS, Smart Logic, Expert Systems, SAP, IBM, FAST,
Concept Searching, Attensity, Clarabridge, Lexalytics
 Projects – Portals, taxonomy, Text analytics – news, expertise location,
information strategy, text analytics evaluation, Quick Start in Text A.
 Clients: Genentech, Novartis, Northwestern Mutual Life, Financial
Times, Hyatt, Home Depot, Harvard Business Library, British Parliament,
Battelle, Amdocs, FDA, GAO, World Bank, etc.
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 Presentations, Articles, White Papers – www.kapsgroup.com
Text Analytics World
Current State of Text Analytics
 History – academic research, focus on NLP
 Inxight –out of Zerox Parc
–
Moved TA from academic and NLP to auto-categorization, entity
extraction, and Search-Meta Data
 Explosion of companies – many based on Inxight extraction with
some analytical-visualization front ends
–




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Half from 2008 are gone - Lucky ones got bought
Early applications – News aggregation and Enterprise Search –
Second Wave = shift to sentiment analysis
Enterprise search – 30-50% of market ($1Bil)
Text Analytics is growing 20% a year, 10% of analytics
Fragmented market – no clear leader
4
Text Analytics World
Current State of Text Analytics: Vendor Space
 Taxonomy Management – SchemaLogic, Pool Party
 From Taxonomy to Text Analytics
– Data Harmony, Multi-Tes
 Extraction and Analytics
– Linguamatics (Pharma), Temis, whole range of companies
 Business Intelligence – Clear Forest, Inxight
 Sentiment Analysis – Attensity, Lexalytics, Clarabridge
 Open Source – GATE
 Stand alone text analytics platforms – IBM, SAS, SAP, Smart
Logic, Expert System, Basis, Open Text, Megaputer, Temis,
Concept Searching
 Embedded in Content Management, Search
– Autonomy, FAST, Endeca, Exalead, etc.
5
Interviews with Leading Vendors, Analysts:
Current Trends
 From Mundane to Advanced – reducing manual labor to
“Cognitive Computing”
 Enterprise – Shift from Information to Business – cost cutting
rather than productivity gains
 Integration – data and text, text analytics and analytics
–
Social Media – explosion of wild text, combine with data – customer
browsing behavior, web analytics
 Big Data – more focus on extraction (where it began) but
categorization adds depth and sophistication
 Shift away from IT – compliance, legal, advertising, CRM
 US market different than Europe/Asia – project oriented
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Enterprise Text Analytics
 Search is still #1 = 30-50% of applications
 New Standard Search – facets (more and more metadata), autocategorization built on taxonomies, clustering
–
Issue – consistent metadata, multiple content sources
 Trend = Text Analytics/Search as Semantic Infrastructure
–
Platform for Info Apps (Search-based applications)
 SharePoint – Major focus of TA companies – fix problems with
taxonomy/folksonomy
–
Hybrid workflow – Publish document -> TA analysis -> suggestions
for categorization, entities, metadata -> present to author
 External information = more automation, extraction – precision
more important
 Use of predictive facets, enhanced relevance (Fast)
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Enterprise Text Analytics
Adding Structure to Unstructured Content
 Beyond Documents – categorization by corpus, by page, sections
or even sentence or phrase
 Documents are not unstructured – variety of structures
– Sections – Specific - “Abstract” to Function “Evidence”
– Corpus – document types/purpose
– Textual complexity, level of generality
 Need to develop flexible categorization and taxonomy – tweets to
200 page PDF
 Applications require sophisticated rules, not just categorization by
similarity
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Enterprise Text Analytics
Document Type Rules
 (START_2000, (AND, (OR, _/article:"[Abstract]",
_/article:"[Methods]“), (OR,_/article:"clinical trial*",
_/article:"humans",
 (NOT, (DIST_5, (OR,_/article:"approved", _/article:"safe",
_/article:"use", _/article:"animals"),
 If the article has sections like Abstract or Methods
 AND has phrases around “clinical trials / Humans” and not words
like “animals” within 5 words of “clinical trial” words – count it and
add up a relevancy score
 Primary issue – major mentions, not every mention
– Combination of noun phrase extraction and categorization
– Results – virtually 100%
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Enterprise Text Analytics
Building on the Foundation: Applications
 Focus on business value, cost cutting
 Enhancing information access is means, not an end
– Governance, Records Management, Doc duplication,
Compliance
– Applications – Business Intelligence, CI, Behavior Prediction
– eDiscovery, litigation support
– Risk Management
– Productivity / Portals – spider and categorize, extract – KM
communities & knowledge bases
• New sources – field notes into expertise, knowledge base –
capture real time, own language-concepts
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Enterprise Text Analytics: Applications
Pronoun Analysis: Fraud Detection; Enron Emails
 Patterns of “Function” words reveal wide range of insights
 Function words = pronouns, articles, prepositions, conjunctions, etc.
– Used at a high rate, short and hard to detect, very social, processed
in the brain differently than content words
 Areas: sex, age, power-status, personality – individuals and groups
 Lying / Fraud detection: Documents with lies have:
– Fewer, shorter words, fewer conjunctions, more positive emotion
words
– More use of “if, any, those, he, she, they, you”, less “I”
 Current research – 76% accuracy in some contexts
– Italian – stylometry – linguistic hedges
 Text Analytics can improve accuracy and utilize new sources
 Data analytics (standard AML) can improve accuracy
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Social Media: Next Generation
Beyond Simple Sentiment
 Beyond Good and Evil (positive and negative)
–
Degrees of intensity, complexity of emotions and documents
 Importance of Context – around positive and negative words
Rhetorical reversals – “I was expecting to love it”
– Issues of sarcasm, (“Really Great Product”), slanguage
–
 Essential – need full categorization and concept extraction
 New Taxonomies – Appraisal Groups – “not very good”
– Supports more subtle distinctions than positive or negative
 Emotion taxonomies - Joy, Sadness, Fear, Anger, Surprise, Disgust
–
New Complex – pride, shame, confusion, skepticism
 New conceptual models, models of users, communities
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Social Media: Next Generation
Behavior Prediction – Telecom Customer Service
 Problem – distinguish customers likely to cancel from mere threats
 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
 More sophisticated analysis of text and context in text
 Combine text analytics with Predictive Analytics and traditional behavior
monitoring for new applications
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Social Media: Next Generation
Variety of New Applications
 Crowd Sourcing Technical Support
User Forums – find problem area, nearby text for solution
– Automatic or Human mediated
–
 Legal Review
Significant trend – computer-assisted review (manual =too many)
– TA- categorize and filter to smaller, more relevant set
– Payoff is big – One firm with 1.6 M docs – saved $2M
–
 Financial Services
–
–
–
–
Trend – using text analytics with predictive analytics – risk and fraud
Combine unstructured text (why) and transaction data (what)
Customer Relationship Management, Fraud Detection
Stock Market Prediction – Twitter, impact articles
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Text Analytics: New Directions
Integration
 Text and Data, Internal and External, Enterprise and Social
 Focus - multiple approaches are needed and multiple ways to
combine
–
Death to the Dichotomies – All of the Above
 Massive parallelism or deeply integrated solution
– Example of Watson - fast filtering to get to best 100 answers,
then deep analysis of 100
 Role of automatic / human
 CRM – struggle to connect to enterprise
– Have to learn to speak “enterprise”
 Imply – Sentiment analysis focus for companies not enough
 Enterprise and Social Media (Delve)
–
Social Media analysis and news aggregation
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Delve for the Web: The Front Page of Knowledge Management
Users follow topics,
people, and
companies selected
from Delve
taxonomies.
Social
media
data from
Twitter
powers
recommen
dation
algorithms.
Text Analytics: New Directions - Integration
Thinking Fast and Slow – Daniel Kahneman
 System 1 – fast and automatic – little conscious control
 Represents categories as prototypes – stereotypes
– Norms for immediate detection of anomalies – distinguish the
surprising from the normal
– fast detection of simple differences, detect hostility in a voice,
find best chess move (if a master)
– Priming / Anchoring – susceptible to systemic errors
– Biased to believe and confirm
– Focuses on existing evidence (ignores missing – WYSIATI)
 .
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Text Analytics: New Directions - Integration
Thinking Fast and Slow
 System 2 – Complex, effortful judgments and calculations
–
System 2 is the only one that can follow rules, compare objects on
several attributes, and make deliberate choices
– Understand complex sentences, validity of logical argument
– Focus attention – can make people blind to all else – Invisible Gorilla
 Similar to traditional dichotomies – Tacit – Explicit, etc
 Basic Design – System 1 is basic to most experiences, and
System 2 takes over when things get difficult – conscious
control
 Text Analysis and Text Mining / Auto-Cat and TA Cat
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Text Analytics: New Directions - Integration
System 1 & 2 – and Text Analytics Approaches
 “Automatic Categorization” – System 1 prototypes
– Limited value -- only works in simple environments
– Shallow categories with large differences
– Not open to conscious control
 System 2 – categories – complex, minute differences, deep
categories
 Together:
– Choose one or other for some contexts
– Combine both – need to develop new kinds of categories
and/or new ways to combine?
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Text Analytics: New Directions - Integration
Text Mining and Text Analytics
 Text Analytics and Big Data enrich each other
–
Data tells you what people did, TA tells you why
 Text Analytics – pre-processing for TM
–
Discover additional structure in unstructured text
– New variables for Predictive Analytics, Social Media Analytics
– New dimensions – 90% of information, 50% using Twitter analysis
 Text Mining for TA– Semi-automated taxonomy development
–
Apply data methods, predictive analytics to unstructured text
– New Models – Watson ensemble methods, reasoning apps
 Extraction – smarter extraction – sections of documents, Boolean,
advanced rules – drug names, adverse events – major mention
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Text Analytics: New Directions - Integration
Integration – Text Analytics and CRM
 Overall – growing demand for natural language processing, TA
–
Identify when a customer is angry or at risk of closing an account
– Growth of regulatory compliance requirements is driving
– Used to understand why people call and whether they were satisfied with the
quality of the experience, diagnose issues and address them
– Combine with Web analytics – need an integrated system
 Contact Center Search – searching and analyzing customer data across
multiple channels – Integration – Salesforce, Coveo, eGain, InQuira
 Enterprise Feedback Management ––want to track satisfaction and
loyalty – issue of unstructured content social media, multimedia channels
 Contact Center Infrastructure – Importance of Cloud based
Services and Infrastructure – Need Semantic Infrastructure
– Cisco – Packaged Contact Center Enterprise
–
 Web Support – virtual agents – deliver one answer to a customer’s
question, not search results list
–
Missing – integrated knowledge management system
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Future of Text Analytics
Obstacles - Survey Results
 What factors are holding back adoption of TA?
–
Lack of clarity about TA and business value - 47%
– Lack of senior management buy-in - 8.5%
 Need articulated strategic vision and immediate practical win
 Issue – TA is strategic, US wants short term projects
–
Sneak Project in, then build infrastructure – difficulty of speaking
enterprise
 Integration Issue – who owns infrastructure? IT, Library, ?
–
IT understands infrastructure, but not text
– Need interdisciplinary collaboration – Stanford is offering EnglishComputer Science Degree – close, but really need a librarycomputer science degree
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Future of Text Analytics
Primary Obstacle: Complexity
 Usability of software is one element
 More important is difficulty of conceptual-document models
–
Language is easy to learn , hard to understand and model
 Need to add more intelligence (semantic networks) and ways for
the system to learn – social feedback
 Customization – Text Analytics– heavily context dependent
– Content, Questions, Taxonomy-Ontology
– Level of specificity – Telecommunications
– Specialized vocabularies, acronyms
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New Directions in Text Analytics
Conclusions
 Text Analytics is growing out (20%) and up – more mature
applications and technique
 Find the right balance of infrastructure and application focus
 Essential theme – integration – text and data, enterprise and
social
 Big obstacles remain
–
Strategic Vision of text analytics in the enterprise
– Concrete and quick application to drive acceptance
 Future – Women, Fire, and Dangerous Things
–
Text Analytics and Cognitive Science = Metaphor Analysis, deep
language understanding, common sense?
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Questions?
Tom Reamy
[email protected]
KAPS Group
http://www.kapsgroup.com
Upcoming: Text Analytics World SF - 2015
Workshop on Text Analytics:
Enterprise Search Summit – New York, May 12-14
Taxonomy Boot Camp, ESS, KMWorld -DC, Nov 4-7
Fall Announcement!