Social Media Analytics Summit April 17-18, 2012 Hotel Kabuki, San Francisco WELCOME TO THE SOCIAL MEDIA ANALYTICS SUMMIT #SMAS12

Transcription

1 Social Media Analytics Summit April 17-18, 2012 Hotel Kabuki, San Francisco WELCOME TO THE SOCIAL MEDIA ANALYTICS SUMMIT #SMAS12

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

3 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 3

4 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, etc. Applied Theory Faceted taxonomies, complexity theory, natural categories, emotion taxonomies Presentations, Articles, White Papers 4

5 Introduction Social Media & Text Analytics Beyond 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 5

6 Introduction Social Media & Text Analytics Beyond 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 6

7 Text Analytics Foundation for Social Media Text 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 7

8 Case Study Categorization & Sentiment 8

9 Case Study Categorization & Sentiment 9

10 10

11 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 11

12 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 12

13 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 13

14 New Applications in Social Media Behavior 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 14

15 New Applications in Social Media Behavior 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 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 15

16 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. 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 16

17 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 17

18 New Directions in Social Media Conclusions 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 18

19 Questions? Tom Reamy KAPS Group Upcoming: Text Analytics Summit Boston - June Text Analytics World Boston October Speakers?