Beyond SentimentMining 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

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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

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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

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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

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Expertise AnalysisAnalytical 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

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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, customers 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

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Beyond Sentiment: Behavior PredictionCase 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

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Beyond SentimentBehavior 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

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Beyond Sentiment - Wisdom of CrowdsCloud / 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

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Beyond SentimentConclusions 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

Questions?

Tom Reamytomr@kapsgroup.com

KAPS Group

Knowledge Architecture Professional Services

http://www.kapsgroup.com