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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 Adding intelligence to BI, CI, and Sentiment

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new directions in social media

New Directions in Social Media

Tom ReamyChief Knowledge Architect

KAPS Group

http://www.kapsgroup.com

agenda
Agenda
  • Introduction – Social Media and Text Analytics
      • Deeper than Positive-Negative
  • Building a Foundation for Social Media
    • Adding intelligence to BI, CI, and Sentiment
  • New Dimensions for Social Media
    • New taxonomies
    • Cognitive Science of Emotion
  • Applications
    • Expertise Analysis, Behavior Prediction, Crowd Sourcing
    • Integration with Predictive Analytics, Social Analytics
  • Conclusions
kaps group general
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:
    • Genentech, Novartis, Northwestern Mutual Life, Financial Times, Hyatt, Home Depot, Harvard Business Library, British Parliament, Battelle, Amdocs, FDA, GAO, World Bank, etc.
  • Applied Theory – Faceted taxonomies, complexity theory, natural categories, emotion taxonomies

Presentations, Articles, White Papers – http://www.kapsgroup.com

introduction social media text analytics beyond simple sentiment
Introduction – Social Media & Text AnalyticsBeyond Simple Sentiment
  • Beyond Good and Evil (positive and negative)
    • 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
  • 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
introduction social media text analytics beyond simple sentiment1
Introduction – Social Media & Text AnalyticsBeyond Simple Sentiment
  • Two basic approaches:
    • 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
text analytics foundation for social media text analytics features
Text Analytics Foundation for Social MediaText Analytics Features
  • Noun Phrase Extraction
    • Catalogs with variants, rule based dynamic
  • Sentiment Analysis
    • Objects and phrases – statistics & rules – Positive and Negative
  • Auto-categorization
    • 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
new dimensions for social media
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
  • Emotion taxonomies
    • Joy, Sadness, Fear, Anger, Surprise, Disgust
    • New Complex – pride, shame, embarrassment, love, awe
    • New situational/transient – confusion, concentration, skepticism
  • Beyond Keywords
    • Analysis of phrases, multiple contexts – conditionals, oblique
    • Analysis of conversations – dynamic of exchange, private language
new applications in social media
New Applications in Social Media
  • Expertise Analysis
    • Experts think & write differently – process, chunks
    • Categorization rules for documents, authors, communities
  • Applications:
    • 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
    • Disgust, shame, cooperation, openness
new applications in social media1
New Applications in Social Media
  • Analysis of Conversations
    • 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+)
  • Experiments - Pronoun Analysis – personality types
  • Essay Evaluation Software - Apply to expertise characterization
    • Model levels of chunking, procedure words over content
new applications in social media behavior prediction telecom customer service
New Applications in Social MediaBehavior Prediction – 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
new applications in social media behavior prediction telecom customer service1
New Applications in Social MediaBehavior Prediction – Telecom Customer Service
  • 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 offor her is going to cancel his act
    • ask about the contract expiration date as she wanted to cxltehacct
  • Combine sophisticated rules with sentiment statistical training and Predictive Analytics and behavior monitoring
new applications wisdom of crowds crowd sourcing technical support
New Applications: Wisdom of CrowdsCrowd 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
new directions in social media text analytics text mining semantic technology
New Directions in Social MediaText Analytics, Text Mining, Semantic Technology
  • Two Systems of the Brain
    • Fast, System 1, Immediate patterns (TM)
    • Slow, System 2, Conceptual, reasoning (TA)
  • Text Analytics – pre-processing for TM, ST
    • 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 – converting text to data
  • Text Mining for TA– Semi-automated taxonomy development
    • Bottom Up- terms in documents – frequency, date, clustering
    • Improve speed and quality – semi-automatic
new directions in social media conclusions
New Directions in Social MediaConclusions
  • Social Media Analysis requires a hybrid approach
    • Software, text analytics, human judgment
    • Contexts are essential
  • Text Analytics needs new techniques and structures
    • Smaller, more dynamic taxonomies
    • Focus – verbs, adjectives, broader contexts, activity
  • Value from Social Media Analysis requires new structures
    • Appraisal taxonomies, emotion taxonomies Plus
    • Better models of documents and authors – multi-dimensional
  • Result:
    • Enhanced sentiment analysis / social media applications
    • Develop whole range of new applications
    • Combine with semantic technology, predictive analytics
questions

Questions?

Tom [email protected]

KAPS Group

http://www.kapsgroup.com

Upcoming: Text Analytics World – Boston, October 3-4

SAS A2012 – Las Vegas, Oct 8-9

Taxonomy Boot Camp – Washington DC, Oct 16-17

Gilbane – Boston, November 27-29

new applications in social media2
New Applications in Social Media
  • Two tasks – emotion detection (distinguish emotion words ), and emotion classification
  • Get references from this article
  • Primary and complex emotions
  • Many states that people call emotion are not in standard taxonomy – ex. Love, confusion, concentration, worry, flirtatiousness, skepticism, indifference
  • Analyzing political trends – augment opinion polls
  • Identifying political bias
new applications in social media3
New Applications in Social Media
  • IDEA – TA needs to grow too – not just nouns, but verbs, adjectives and adverbs, phrases, etc.
  • Text to Emotion Engine
  • Uses basic emotion taxonomy – happy, sad, fear, surprise, anger, disgust
  • Identify conditional sentences – “I’m happy when he is here”
  • Emotion intensity – adjectives – very, eetc.
new applications in social media4
New Applications in Social Media
  • “Most expressions of feeling can be more properly situated within a context of exchange,”
  • People use a variety of techniques to establish intimacy in epistolary exchanges:
  • Self-revelation, humor, sharing of secrets, establishment of informal agreements, private language
  • Can be applied to bodies of documents like corporate email exchanges to determine such features:
  • De facto hierarchy of an organization (Me – add expertise?), strength or weakness of social ties, changing relationships among co-workers over time
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