This presentation was prepared for a distance learning class on Sentiment Analysis and how insights from it can be used for marketing strategy & program development.

1. Sentiment Analysis Overview
October 12, 2012
© 2010 IBM Corporation

2. Susan Emerick
Program Manager, Social Business Enablement
Digital & Social Influence Strategy & Development, IBM CHQ
Susan Emerick is a seasoned integrated marketing communications consultant with deep expertise in Digital & Social
Influence Marketing Strategy. She has a proven track record developing effective marketing programs utilizing the best mix
of on-line marketing techniques, exploiting new market opportunities created by the worldwide adoption of social media,
mobile and other emerging technologies.
In her current role, Susan is dedicated to evolving marketing as a practice, articulating the benefits of integrating digital and
social influence programs to foster long-term, high value relationships with clients, prospects, partners, colleagues, and
communities. She currently leads many of IBM’s transformational workforce initiatives which empower IBMers across the
globe to deliver business value through sharing their expertise across the social web.
Beginning in 2008, Susan was instrumental in establishing IBM’s C.O.R.E. (Cross functional, On-going Research &
Engagement) Social Marketing practice in partnership with the IBM Market Insights team. This social marketing
methodology was founded on gathering social “listening” intelligence through on-line research and applying key insights to
marketing planning and social engagement strategies. This IBM initiative was awarded the 2010 SAMMY (Social
Advertising, Media and Marketing) for “Best Socialized Business”
Susan enjoys sharing her expertise with other marketing professionals by speaking at a range of leading industry
conferences including, Word of Mouth Marketing Association (WOMMA) School of WOM, iMedia Summit and DMAD.
Prior to joining IBM, Susan led the development of global marketing programs for both B2B & Consumer brands across
various industries including Financial Services, Media & Entertainment and Retail Distribution.
LinkedIn: www.linkedin.com/in/sfemerick
Twitter: @sfemerick
Blog: www.susanemerick.com
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© 2010 IBM Corporation

3. Amy A. Laine
Principal Market Analyst, Team Lead
Client Research, IBM CHQ Market Insights
Amy A Laine is a Principal Market Analyst and Team Lead within IBM's Market Insights division. She leads the Market
Trends and New Opportunities Program, and is currently driving targeted research to best inform business decisions as IBM
embraces the digital marketplace.
Amy is a founding partner in the design of IBM’s C.O.R.E. (Cross functional, On-going Research & Engagement) social media
program – a program that embeds research as the foundation for an enterprise-wide platform for a social media strategy
built on planning and engagement.
Having been with IBM for over a decade, always within the market intelligence/market insights division, Amy believes that
starting with research and incorporating continued measurement throughout the execution or active outreach phases is
critical for a successful social strategy meant to transform the business.
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© 2010 IBM Corporation

4. Sentiment Analysis: What is it and General Uses
What is sentiment analysis?
“Automated sentiment analysis is the
process of training a computer to identify
sentiment within content through Natural
Language Processing (NLP). Various
sentiment measurement platforms
employ different techniques and
statistical methodologies to evaluate
sentiment across the web. Some rely
100% on automated sentiment, some
employ humans to analyze sentiment,
and some use a hybrid system.”
- Maria Ogneva is the Director of Social Media at
General uses for sentiment analysis
– Brand Health Monitoring
– Competitive Positioning
– Predictive Analytics and Modeling
– Customer Advocacy:
• Customer value
• Customer attribution
– Customer Pain Points
– Marketing planning and segmentation
– Campaign effectiveness
Biz360
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© 2010 IBM Corporation

5. There are several key steps to harvesting insights from the Digital
Marketplace…
DEFINE keywords
MINE publicly available social
media data within specific dates
based on keyword relevance
FORUMS / NEWSGROUPS
VIDEO SHARING
ANALYZE data via human analysts
WIKIS
ESTABLISH benchmark metrics
MICROBLOGS
Sentiment
analysis of
publicly available
content
SOCIAL NETWORKS
SOCIAL MEDIA NEWS
AGGREGATORS
PHOTO SHARING
* OR *
EVALUATE performance against
benchmark
BLOGS
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© 2010 IBM Corporation

6. The first step, keyword definition, is of critical importance to the
quality of the insights - and decision-making - based on the data
In a world with a billion computers, four billion cell phones and a robust global Internet, there is an
overwhelming amount of digital messages being posted online every day
Most are not relevant to your brand or specific topic of interest. Within those that are, not all are
relevant
Establishing pre-defined keywords allow us to narrow down the universe of all possible posts to
only those that are relevant to our research needs
Much like developing a screener to determine who you want to invite to a focus group (e.g., “Large
Enterprise”“IT professionals” who are “hardware purchase decision-makers” in the “U.S.”), we need to
determine the criteria for inclusion in the listening sample set by defining the keywords that signal: Include
this POST in data collection
If the keywords are too broad, then we get “noise” (i.e., irrelevant posts)
If they are too narrow, then we miss relevant conversation and may draw erroneous conclusions
Filter Universe of all posts
EVERY POST
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Include Unique Posts
RELEVANT POSTS
© 2010 IBM Corporation

7. Keywords often need to be refined and qualified in different ways…
For conversation mining, several “strings” can be employed:
– A category string designed to pull in discussion relevant to a specific server
– A branded string designed to pull in mentions of IBM within the larger server discussion
– A category string designed to pull in mentions of specific products within the larger server
discussion
The category string is shaped into a Boolean keyword string*
By zeroing in on the terminology that buyers and decision makers actually use, we will best capture
their online conversations
Server A
IBM
+
Server A
Product T
Product U
Server B
Server B
+
Product V
Produce W
Product X
Product Y
Server C
Server C
*Note: Boolean Keyword String – A set of keywords that employs Boolean logic to focus and return specific , relevant messages in search
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© 2010 IBM Corporation

8. The analysis focuses on what you want to know. The overall
conversational volume will show peaks during product launches
Volume
1Q
2Q
3Q
• Volume is based on keyword matches
• Volume is measured on the record level
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© 2010 IBM Corporation

9. But who is contributing to the conversation is of critical
importance when analyzing the online discussion as well
“Voice”
Media / Press
Professionals
1Q
2Q
3Q
Employees
Consumers
Investors
Competitors
Analysts
Business Partners
Executives
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10. And it is not only how much is said, but why people are contributing?
Conversation Volume by Message Type
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11. And where they are talking…
Conversation Volume by Venue
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12. It is also valuable to understand how conversational themes are
related to one another.
Conversation Topic Relationships, Volume and Sentiment
Server B Performance
User Preference
Product V Performance
Events
Product W
Interoperability
Industry News
Announcements
Cost/Affordability
Product T Support
Server B
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Source: Converseon, 2010 Sentiment Analysis Research Study
IBM Confidential
Security
Server C
Server Migration
Scalability
© 2009 IBM Corporation

13. Word clouds provide an understanding of the most often mentioned
terms within the relevant buzz
Word Cloud of General Online Conversation
• Word size corresponds with frequency of occurrence within the data set
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Source: Converseon, 2010 Sentiment Analysis Research Study
© 2009 IBM Corporation

14. Closing thoughts on top trends and what’s on the horizon ….
Influence: out of all the posts about your brand, how do you pick the top 50 to focus on and
connect with - relationship management and priority coverage modeling
Reputation management: Establishing prominence, reputation management - focused
enablement of employees to build influence as a reputable authority in relevant conversation
Enterprise wide response management: You may hear this referred to as Social CRM, tying
social dialogue into customer relationship management systems
Real time, Predictive, Actionable, anticipating customers needs and be more responsive to
improved customer experience
Business Transformation, from social media primarily considered “consumer oriented
networking” to applications in Social Collaboration and Social Networking to achieve
Business outcomes
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15. Thank You !
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16. Open Forum : Q&A
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