Power Point from a Webinar which focused on Sentiment Analysis

1. Gary Angel and Scott K. Wilder

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Gary Angel, President of Semphonic
Co-Founder and President of Semphonic, the leading independent web
analytics consultancy in the United States. Semphonic provides full-
service web analytics consulting and advanced online measurement to
digital media, financial services, health&pharma, B2B, technology, and
the public sector. Gary blogs at http://semphonic.blogs.com/semangel
Introductions
Scott K. Wilder, Digital Strategist, WilderVoices
Recently was SVP/Social Media Architect at Edelman – Digital. Founded and
managed Intuit’s Small Business Online Community and Social Programs.
Before Intuit, Scott was the VP of Marketing and Product Development at
Kbtoys / eToys, the founder and director of Borders.com, and held senior
positions at Apple, AOL, and American Express. Scott is also a founding
Board member of the Word of Mouth Marketing Association. He received
graduate degrees from New York University, The Johns Hopkins University
and Georgetown University. Scott just jump started his own business,
“WilderVoices” and blog @ www.wildervoices.com

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This will be a good webinar if….
you come away with a sense of
how to do sentiment analysis,
And how to make it
work for your organization!!

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Sentiment Analysis – Complex Definition
Sentiment analysis or opinion mining refers to a broad
(definitionally challenged) area of natural language
processing, computational linguistics and text mining.
General speaking, it aims to determine the attitude of the
speaker or writer with some respect to some topic. The
attitude maybe their judgment or evaluation, their
affective state – that is to say, the emotional state of the
author when writing – or the intended emotional
communication – that is to say, the emotional affect the
author wants to have on the reader

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Sentiment Analysis – Simple Definition
The simplest algorithms work by scanning keywords
to categorize a statement as positive or negative,
based on a simple binary analysis:
-“love” is good,
-“hate” is bad,
-“I don’t know,” which means there
probably will not be a second date

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Why Brands and Agencies Like Sentiment Analysis
• Takes time to read and review every verbatim
• Numbers look sexy
• Can observe changes in issues / topics over time

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How most agencies present Sentiment Analysis

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Or they just show you this..

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Sentiment Analysis and Market Research
Market Research
Anecdotal
Focus
Groups
Comment
Cards
Behavioral
Purchase
Data
Web
Analytics
Primary
Research
Online
Survey
Traditional
Survey
Sentiment Analysis

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Anecdotal Research
Good For Not

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Behavioral Research
Good For Not

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Primary Research
Good For Not
Thematic
Tuning
Color
Behavioral
Impact
Audience
Segmentation
Campaign
Development
Message
Tuning

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Usage of Sentiment Analysis
• Sentiment Analysis is Anecdotal
• Sentiment Analysis is Behavioral
• Sentiment Analysis IS NOT Primary Research
• NOTE: YOU CAN NOT AND SHOULD NOT ASSUME
THAT FINDINGS FROM SENTIMENT ANALYSIS
ARE REPRESENTATIVE OF YOUR ENTIRE
AUDIENCE.

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Usage of Sentiment Analysis
• Identify how people talk about your product
• Understand how people talk about your
category
• Learn more about the competition
• Respond real time to what people are saying
Anecdotal
• Learn more about the competition
• Track changes in perception over timeBehavioral

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Sentiment Analysis for Anecdotal Research
Manual
• Themes and Colors emerge subjectively
• Word Clouds are the closest data technique
Non-Aggregated
• It’s the actual text that matters
• Sentiment Analysis can help cull non-neutral
messages
Classification
• Subjective vs. Objective can help refine

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Sentiment Analysis for Behavioral Research
Automated?
• Human analysis of sentiment is more
accurate
• Consider analysis of API data using 3rd
Party tools
Sub-Classification
• Sentiment classification is easier and
more tunable in narrow bands of
meaning
• Sub-classify products etc.

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Listening Platforms
• Buzz Metrics
• Radian6
• ScoutLabs
• Sysomos
• Techrigy’s

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Text Mining Systems
• SAS
• Endeca
• Autonomy
• Lexalytics
• OpenAmplify
• You can get direct feeds to many social systems and mine text directly
using solutions that are more powerful and tunable than those
contained in most listening tools.

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The Right Tool Depends on the Data
• If your analyzing tweets, you need a tool that
understands acronyms and emoticons.
• Sentence analysis is different that article analysis.
Twitter is more like sentence analysis.
• Topical classification, Objective v. Subjective, and
Sentiment (polarity) are fundamentally different types
of analysis.

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An example of the Problem:
• Major software company looked at their major brand
• 80% of the comments were neutral
• 61% of the posts marked with a positive or negative
sentiment came from microblogs.
• They were data oriented, general information
• But brands look at ‘Neutral’ and now what??

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Issues
• Unreliable – not as good as pure human primary research
• Not sure what to do with Neutral
• No tool consistently identifies positive and neutral
• Inconsistency across tools
• Power of one or a small group of influencers
• Lack of Accuracy

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Issues
• Inconsistent across social networks (Twitter is usually
60% neutral)
• Difficult to determine target
• Overlook actual verbatim
• No standards

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Now What?
So…Now What?

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10 Steps to find the Diamond
1 Decide on internal customer - which business unit, group will use data
2 Identify and use one tool (standardize on governance and set up)
3 Combine with other metrics/KPIs
4 Determine how broad you will go, but only look at one platform, community
at a time
5 Identify emerging, trending topics
6 Take % of positive, neutral and negative
7 Take random sample of a 100 quotes from positive, neutral, negative, groups
8 Read verbatim
9 Categorize issues (For Intuit: Set up software vs. invoices vs. install vs.
customer service)
10 Compare with other biggest competitors

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Old School
• More skin in the game
• More engaged leads to greater satisfaction
and places you closer to users/people
• More confidence in results
• Don’t drink and drive (I mean, don’t mix
platforms)

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Note: It’s Never accurate
Note: It’s never totally accurate

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• This presentation will be posted @
> http://www.semphonic.com
>www.slideshare.net/skwilder
>www.wildervoices.com
Thank you for your time

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For more information
Gary Angel:
gangel@semphonic.com
Blog: http://semphonic.blogs.com/semangel/
@garyangel
Scott K. Wilder
scott@wildervoices.com
New blog: http://www.wildervoices.com
@skwilder
For other presentations:
http://www.slideshare.net/skwilder