sentimental market segmentation
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Sentimental Market Segmentation
Shlomo Argamon, Ph.D.
CEO & Cofounder, Subtext3
Based on a presentation given at
Sentiment Analysis SymposiumApril 13, 2010, New York, NY
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Lots of sentiment analysis
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Why??Lots of sentiment analysis
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Why sentiment analysis
What are they thinking?
What do they want?
What will they buy?
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Wheres the ROI?
What should I fix?
Find comparatively negative aspects of my
product or positive areas about competitors
Howm I doing?
Examine sentiment trends to examine effects of
marketing or new products
Wheres the action? Find the customersunfulfilled needs
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Perceptions
Choices
Products &
Features
Advertising
Potential
Customer
Opinions
Texts
Needs &
Wants
Customer Model
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PerceptionsProducts &
Features
Advertising
Potential
Customer
Opinions
Texts
Needs &
Wants
Choices
Customer Model
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Products &
Features
Advertising
Potential
Customer
Opinions
Texts
Needs &
WantsPerceptions
Perceptions
Perceptions
Choices
Customer Model
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Products &
Features
Advertising Opinions
Texts
Needs &
WantsPerceptions
Perceptions
Perceptions PotentialCustomer
Potential
Customer
Potential
Customer
Potential
Customer
Choices
Customer Model
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Products &
Features
Advertising Opinions
Texts
Needs &
WantsPerceptions
Perceptions
Perceptions PotentialCustomer
Potential
Customer
Potential
Customer
Potential
Customer
Choices
Customer Model
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Market Segmentation
Product/brand segmentation
What products are close to which?
Based on customer needs and perceptions, notproduct features
Customer/community segmentation
Meaningful subsets of potential customers
Relative to a given market!
Know their characteristics
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Perceptual Maps
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CommunityMaps
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Understand The Community
Not just whatthey are sayingWhois saying it?
What groups of people have similar
opinions (about X)?
What kinds of people are they?
Howdo they see things? How do they group products, brands, or
features?
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Varieties of Sentiment Analysis
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DocumentFilter(topic, source)
SentimentClassifier
Positive
Negative
Neutral
Trend
Snapshot
Multidimensional
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Sentiment
Finder
Topic
Classifier
Topic/Sentiment
Correlation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Coke Pepsi RC
Neutral
Negative
Positive
Trend
Snapshot
Multidimensional
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Sentiment
Finder
Target
Finder
Target/Sentiment
Correlation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Coke Pepsi RC
Neutral
Negative
Positive
Trend
Snapshot
Multidimensional
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But
Who are they and what do they think
We still need
More detail on their opinions
Profiles of the writers
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Detailed
Sentiment
Finder
Target
Finder
Sentiment
Complexes
Demographic
Profiling
Demographic Trends
Perceptual Map
Customer Map
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Detailed Sentiment Finding
Based on research in the Linguistic Cognition Lab
at Illinois Institute of Technology
http://lingcog.iit.edu
http://lingcog.iit.edu/http://lingcog.iit.edu/ -
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Different kinds of sentiment
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Different kinds of sentiment
This uniquedog or chicken housewasmade with green practices in mind.
PositiveAppreciation
Valuation
Oh, and on principle Ihateextremists /zealotsof any ilk.
NegativeAffect
Affection
...if your sitelooks a complete mess then
you will loose[sic]that visitor for good.
NegativeAppreciation
Quality
Oh and Spooks, with this really tough
and sexy old ladyin it, kicking ass.
PositiveJudgment
Tenacity
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Syntactic connections
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More sentiment expressions
[I]evaluator[couldnt]polaritybring myselfto [like]attitude
[him]target.
[It]target-1is [not]polarity[as [good]attitudeas]comparator[theMinolta D7]target-2.
[Gap.Com]targetis an [excellent]attitudeexample of [aretailer]superordinate[using its online shopping store as an
extension and expansion of its retailing]aspect.
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Detailed sentiment finding
Texts
LexiconLinkage
Rules
ComplexSentiment
Expressions
Dependency
Parsing
Syntactic
Relations
Find
Chunks
(attitudes,targets,
hinges,)
ChunksExpression
LinkageDisambiguation
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Demographic Profiling
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Authorship Profiling
Infer things about the author from the style of
the language
Gender
Age
Native language
Personality type
Education level
Etc
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Capturing language style
Linguistic variation independent of the topic Function words
Parts-of-speech
Syntactic structures Morphology
Linguistic complexity
Vocabulary size
Mistakes
Slang
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Male/Female Classification
20th Century narrative fiction: 79%
20th Century non-fiction: 83%
21st Century blogs: 77%
17th-19th Century French lit.: 76%
Male Features Female Features
the, this, that, those,as, one, of, to,
prepositionsadjectives numbers
she, for, with, not, and,in, I, you, pronounspresent-tense-verbs
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Other dimensions
Native language: ~80%
Personality:
Neuroticism: ~68%
Extraversion: ~55-70%
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Simple Prototype
53,983 blog snippets from the ICWSM task
corpus (Aug-Sep, 2008)
268,665 sentiment expressions found
Examples:
I think that Sarah [Palin]targetwould be a
[terrible]attitude[vice president]superordinate
[the game]targetwas [too simplistic]attitude[to serve as
proper material for argument]aspect
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Authorship Profiling
Gender (Male/Female)
Age (Younger/Older)
Based on features from previous studies
Education level (LowerEd, MediumEd, HigherEd)
Based on linguistic complexity
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Trend Analysis
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Apartment sentiment by gender
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apartment/Female
apartment/Male
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Apartments - gender difference
apartment/Male-Female
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McCain & Palin - gender difference
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Date
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mccain/Male-Female
palin/Male-Female
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Perceptual Map - Relationships
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Perceptual Map - Relationships
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Perceptual Map - Issues
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Sentimental Market Segmentation
Construct perceptual and community maps, by: Detailed extraction of sentiment expressions
Semantic and structural detail
Authorship profilingTells us what kinds of people are writing which opinions
(Also need to attribute third-party sources)
Dimensionality reduction over author opinions and
profiles (PCA, MDS, etc.)
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Thank you
Contact us:
Shlomo Argamon
[email protected]@ShlomoArgamon
http://www.subtext3.com