carnegie mellon sentiment analyses overview

© 2010 IBM Corporation

Sentiment Analysis Overview

October 12, 2012


Susan EmerickProgram Manager, Social Business EnablementDigital & 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

2


Amy A. LainePrincipal Market Analyst, Team LeadClient 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.

3


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

Biz360

�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

4


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

� ANALYZE data via human analysts

� ESTABLISH benchmark metrics

* OR *

� EVALUATE performance against

benchmark

SOCIAL NETWORKS WIKIS

PHOTO SHARING

BLOGS

MICROBLOGS

FORUMS / NEWSGROUPS

VIDEO SHARING

SOCIAL MEDIA NEWS

AGGREGATORS

Sentiment analysis of

publicly available content

5

© 2010 IBM Corporation6

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

Include Unique PostsFilter Universe of all posts

EVERY POST RELEVANT POSTS


7

Keywords often need to be refined and qualified in different ways…

Server A

Server B

*Note: Boolean Keyword String – A set of keywords that employs Boolean logic to focus and return specific , relevant messages in search

� 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

+IBM Server A

Server B

Product T

Product U

Produce W

Product X

+Product V

Product Y

Server C Server C


• Volume is based on keyword matches

• Volume is measured on the record level

The analysis focuses on what you want to know. The overall conversational volume will show peaks during product launches

8

1Q 2Q 3Q

VolumeVolume


Media / Press

Professionals

Employees

Consumers

Investors

Competitors

Analysts

Business Partners

Executives

But who is contributing to the conversation is of critical importance when analyzing the online discussion as well

9

1Q 3Q2Q

“Voice”“Voice”

© 2009 IBM Corporation10 IBM Confidential

And it is not only how much is said, but why people are contributing?

Conversation Volume by Message TypeConversation Volume by Message Type

10

© 2010 IBM CorporationIBM Confidential

And where they are talking…

Conversation Volume by VenueConversation Volume by Venue

11

© 2009 IBM Corporation12 IBM Confidential

It is also valuable to understand how conversational themes are related to one another.

Conversation Topic Relationships, Volume and SentimentConversation Topic Relationships, Volume and Sentiment

Product W

Cost/Affordability

Events

Industry News Announcements

User Preference

Product T Support

Server Migration

Server B Performance Product V Performance

12

Server B

Server CInteroperability

Scalability

Security

Source: Converseon, 2010 Sentiment Analysis Research Study


Word clouds provide an understanding of the most often mentionedterms within the relevant buzz

Word Cloud of General Online ConversationWord Cloud of General Online Conversation

13

• Word size corresponds with frequency of occurrence within the data set

Source: Converseon, 2010 Sentiment Analysis Research Study


Closing thoughts on top trends and what’s on the horizon ….

14

� 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


Thank You !

15


Open Forum : Q&A

16