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New Directions in Social Media. Tom Reamy Chief Knowledge Architect KAPS Group 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


  • 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 –

    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


    • 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




    KAPS Group

    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