Transcript Taxonomy Development Workshop

New Directions
in
Social Media
Tom Reamy
Chief Knowledge Architect
KAPS Group
http://www.kapsgroup.com
Agenda
 Introduction – Social Media and Text Analytics
 Deeper than Positive-Negative
 Building a Foundation for Social Media
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Adding intelligence to BI, CI, and Sentiment
 New Dimensions for Social Media
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New taxonomies
– Cognitive Science of Emotion
 Applications
– Expertise Analysis, Behavior Prediction, Crowd Sourcing
– Integration with Predictive Analytics, Social Analytics
 Conclusions
2
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 Fast Start – Audit, Evaluation, Pilot
– Social Media: Text based applications – design & development
 Clients:
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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 – http://www.kapsgroup.com
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Introduction – Social Media & Text Analytics
Beyond Simple Sentiment
 Beyond Good and Evil (positive and negative)
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Social Media is approaching next stage (growing up)
– Where is the value? How get better results?
 Importance of Context – around positive and negative words
Rhetorical reversals – “I was expecting to love it”
– Issues of sarcasm, (“Really Great Product”), slanguage
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 Granularity of Application
Early Categorization – Politics or Sports
Limited value of Positive and Negative
– Degrees of intensity, complexity of emotions and documents
 Addition of focus on behaviors – why someone calls a support center
– and likely outcomes
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
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Introduction – Social Media & Text Analytics
Beyond Simple Sentiment
 Two basic approaches:
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Statistical Signature of Bag of Words
– Dictionary of positive & negative words
 Beware automatic solutions – Accuracy, Depth
 Essential – need full categorization and concept extraction to get
full value from social media
 Categorization - Adds intelligence to all other components –
extraction, sentiment, and beyond
 Categorization/extraction rules – not just topical or sentiment
 Combination with advanced social media analysis
 Opens up whole new worlds of applications
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Text Analytics Foundation for Social Media
Text Analytics Features
 Noun Phrase Extraction
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Catalogs with variants, rule based dynamic
 Sentiment Analysis
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Objects and phrases – statistics & rules – Positive and Negative
 Auto-categorization
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
Training sets, Terms, Semantic Networks
– Rules: Boolean - AND, OR, NOT
– Advanced – DIST(#), ORDDIST#, PARAGRAPH, SENTENCE
Text Analytics as Foundation
– Disambiguation - Identification of objects, events, context
– Build rules based, not simply Bag of Individual Words
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Case Study – Categorization & Sentiment
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Case Study – Categorization & Sentiment
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New Dimensions for Social Media
 New Taxonomies – Appraisal
Appraisal Groups – Adjective and modifiers – “not very good”
– Four types – Attitude, Orientation, Graduation, Polarity
– Supports more subtle distinctions than positive or negative
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 Emotion taxonomies
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Joy, Sadness, Fear, Anger, Surprise, Disgust
– New Complex – pride, shame, embarrassment, love, awe
– New situational/transient – confusion, concentration, skepticism
 Beyond Keywords
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Analysis of phrases, multiple contexts – conditionals, oblique
Analysis of conversations – dynamic of exchange, private language
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New Applications in Social Media
 Expertise Analysis
Experts think & write differently – process, chunks
– Categorization rules for documents, authors, communities
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 Applications:
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–
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Business & Customer intelligence, Voice of the Customer
Deeper understanding of communities, customers – better models
Security, threat detection – behavior prediction, Are they experts?
Expertise location- Generate automatic expertise characterization
 Behavior Prediction–TA and Predictive Analytics, Social Analytics
 Crowd Sourcing – technical support to Wiki’s
 Political – conservative and liberal minds/texts
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Disgust, shame, cooperation, openness
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New Applications in Social Media
 Analysis of Conversations
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Techniques: self-revelation, humor, sharing of secrets,
establishment of informal agreements, private language
– Detect relationships among speakers and changes over time
– Strength of social ties, informal hierarchies
 Combination with other techniques
Expertise Analysis – plus Influencers
– Quality of communication (strength of social ties, extent of private
language, amount and nature of epistemic emotions – confusion+)
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 Experiments - Pronoun Analysis – personality types
 Essay Evaluation Software - Apply to expertise characterization
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Model levels of chunking, procedure words over content
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New Applications in Social Media
Behavior Prediction – Telecom Customer Service
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Problem – distinguish customers likely to cancel from mere threats
Analyze customer support notes
General issues – creative spelling, second hand reports
Develop categorization rules
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–
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First – distinguish cancellation calls – not simple
Second - distinguish cancel what – one line or all
Third – distinguish real threats
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New Applications in Social Media
Behavior Prediction – Telecom Customer Service
 Basic Rule
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(START_20, (AND,
–
(DIST_7,"[cancel]", "[cancel-what-cust]"),
– (NOT,(DIST_10, "[cancel]", (OR, "[one-line]", "[restore]", “[if]”)))))
 Examples:
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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 and behavior monitoring
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New Applications: 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.”
–
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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
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New Directions in Social Media
Text Analytics, Text Mining, and Predictive Analytics
 Two Systems of the Brain
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Fast, System 1, Immediate patterns (TM)
– Slow, System 2, Conceptual, reasoning (TA)
 Text Analytics – pre-processing for TM
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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
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Bottom Up- terms in documents – frequency, date, clustering
Improve speed and quality – semi-automatic
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New Directions in Social Media
Conclusions
 Social Media Analysis requires a hybrid approach
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Software, text analytics, human judgment
– Contexts are essential
 Text Analytics needs new techniques and structures
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Smaller, more dynamic taxonomies
Focus – verbs, adjectives, broader contexts, activity
 Value from Social Media Analysis requires new structures
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Appraisal taxonomies, emotion taxonomies Plus
– Better models of documents and authors – multi-dimensional
 Result:
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Enhanced sentiment analysis / social media applications
– Develop whole range of new applications
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Questions?
Tom Reamy
[email protected]
KAPS Group
http://www.kapsgroup.com
Upcoming: Text Analytics Summit – Boston - June
Text Analytics World – Boston – October – Speakers?