1. Beyond Sentiment Mining Social Media A Panel Discussion of Trends and Ideas Marie Wallace, IBM Marcello Pellacani, Expert System Fabio Lazzarini, CRIBIS D&B Moderator: Tom Reamy, KAPS Group

2. 2 Agenda  Introduction  Quick Overview – Tom Reamy, KAPS Group, Moderator – Expertise Analysis and Beyond – Marie Wallace, IBM – Semantic Technologies Allow Us to Harness the Collective Knowledge of Social Media – Marcello Pellacani, Expert Systems – Listening to the Voice of the Customer – Fabio Lazzarini, CRIBIS D&B – Listening to the Voice of the Customer  Questions and Discussion

3. 3 KAPS Group: General  Knowledge Architecture Professional Services  Virtual Company: Network of consultants – 8-10  Partners – SAS, Smart Logic, Microsoft-FAST, Concept Searching, etc.  Consulting, Strategy, Knowledge architecture audit  Services: – Text Analytics evaluation, development, consulting, customization – Knowledge Representation – taxonomy, ontology, Prototype – Metadata standards and implementation – Knowledge Management: Collaboration, Expertise, e-learning – Applied Theory – Faceted taxonomies, complexity theory, natural categories

4. 4 Beyond Sentiment: Expertise Analysis  Apply Sentiment Analysis techniques to Expertise  Expertise Characterization for individuals, communities, documents, and sets of documents  Experts prefer lower, subordinate levels – Novice prefer higher, superordinate levels – General Populace prefers basic level  Experts language structure is different – Focus on procedures over content  Types of expert – technical, strategic

5. 5 Expertise Analysis Analytical Techniques  Corpus context dependent – News versus scientific health care context – Need to generate overall expertise level for a corpus  Also contextual rules – “Tests” is general, high level – “Predictive value of tests” is lower, more expert  Develop expertise rules – similar to categorization rules – Use basic level for subject – Superordinate for general, subordinate for expert

6. 6 Expertise Analysis Expertise – application areas  Taxonomy / Ontology development /design – audience focus – Card sorting – non-experts use superficial similarities  Business & Customer intelligence – add expertise to sentiment – Deeper research into communities, customer s  Text Mining - Expertise characterization of writer, corpus  eCommerce – Organization/Presentation of information – expert, novice  Expertise location- Generate automatic expertise characterization based on documents  Experiments - Pronoun Analysis – personality types – Essay Evaluation Software - Apply to expertise characterization Model levels of chunking, procedure words over content

7. 7 Beyond Sentiment: Behavior Prediction Case Study – 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

8. 8 Beyond Sentiment Behavior Prediction – Case Study  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

9. 9 Beyond Sentiment - Wisdom of Crowds Cloud / 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

10. 10 Beyond Sentiment Conclusions  Sentiment Analysis needs good categorization  Expertise Analysis can add a new dimension to sentiment – More sophisticated Voice of the Customer  Multiple Applications from Expertise analysis – search, BI, CI, Enterprise Content Management, Expertise Location  New Directions – Behavior Prediction, Crowd Sourcing, ?  Text Analytics needs Cognitive Science – Not just library science or data modeling or ontology

11. Questions? Tom Reamy tomr@kapsgroup.com KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com