Leveraging Big Data to Derive Actionable People Insights
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Transcript of Leveraging Big Data to Derive Actionable People Insights
PowerPoint Presentation
Leveraging Big Data to Derive
Actionable People Insights
Huahai YangUSER Group, Computer Science
About Us
Aka. IBM Almaden Research Center (ARC)
On top of a hill in the southern tip of Silicon Valley
About Us
ARCScience & Technology
Storage Systems
Service Science Research
Computer ScienceTheory
Database Management
Intelligent Information System
Healthcare Information Technology
User System and Experience Research (USER)Currently led by Michelle X. Zhou
Join us, we are always hiring!Interns, Postdocs, Software Engineers and Research Staff Members
Big Data Opportunities
IndustriesFinance
Retail
Product Manufacturer
Tel-communication
Entertainment
PotentialsCustomer acquisition/retention
Market segmentation
Brand management
Risk assessment
300+ million tweets daily
1+ million blog posts daily
Our Focus: Insights about People
Perceptions, Sentiments, Personalities and other profiles
crowdsourcing2
480px-Twitter_2010_logoblogger-logo54px-Digg-new266px-FacebookTumblr-logoWordpress-logoFlixsterlogo
Outline
Consumer usersOpinionBlocksVisually summarizing product reviews
Business usersBrandy Understanding user perception and personality for direct marketing
qCrowdActively engaging individuals on social media
OpinionBlocks Motivation
Online product reviews Significant in consumer decision making
Large volume, uncurated
Limited search tools
Users have different priorities
Help consumer makes better use of reviews
Difficulties with Review Text
A lot of variations in terms of: length
clarity of language
to the point vs vague
emotional vs subjective
Incoherent with the rating
Redundant
Prior Work
Chen et al. Visualizing Analysis of Conflicting Opinions, 2006
Oelka et al. Visual Opinion Analysis of Customer Feedback Data, 2009
Analyze first, and visualize the analysis results only
Can Consumer Trust the Analysis?
Sentiment analysis is not a solved problem in NLPOften less than 80% accuracy
Aspect oriented sentiment is even less accurate
Low resolutionOften polarity only: positive, negative, neutral
Not very actionable
it was not clean, but I am not expecting a better performance from any vacuum.
Our Approach
Support an interactive reading experience where users can search for relevant information.
Visualize the text itself while highlighting analysis results.
Show the categorized text in context so that users can judge fairness of the sentiment analysis.
Progressively disclose textual information while continuously providing visual graphical summaries
Overview First
Provide summary of overall opinion
Identify important features and key issues in each
Interactivity reveals correlations among features
Filter on Demand
Polarity of feature
Keywords
Snippets
Zoom across LODs
Zoom across LODs
Work in Progress
Formal user studiesDoes the system help consumers?Learn better about the product domain
Find information faster
Make better decisions
Resign of the UICompare products
Scalable with larger number of reviews
Brandy
360 understanding of a business brand: evidence-based brand management from social media
Brand associations (e.g., key aspects)
Competitive brands
Brand evolution
User modeling of those who voiced brand perception
Demographics, personality traits, locations, brand association and sentiments
Active brand management for effective marketing
Craft/adjust marketing messages based on brand analysis
Associations and customer needs
Deliver marketing messages to target customers
Individual customers (e.g., customer retention)
Customer segments (e.g., customer acquisition)
Social Data
Enriched/newCustomer Profile
Segment-based Direct Marketing(Customer Acquisition)
BrandManagement(Marketing Research)
Existing Customers
New Customers
Social Data of Known Customers
PerceptualMap
Social Data of Unknown Customers
(A) Profiling Fusion
(C) Brand perception from social data
(D) Overlay
Unica (Today)
(B) User profiling from social data
CustomerProfiles
Individual-basedDirect Marketing(Customer retention)
Data
Key Technology
Applications
Project Map
(E)
(F)
(G)
G is a must, and E&F are bonus
Features
#
Examples
Computation
LIWC
(dictionary-based measurement of aspects of word usage)68
First personNegationFeelingCommunicationLeisureDeath
Let g be a LIWC category, Ng denotes the number of occurrences of words in that category in ones tweets and N denotes the total number of words in his/her tweets. A score for category g is then: Ng/N.
Big Five(personality types, OCEAN)
5
Openness
Using correlations with LIWC features as reported by previous researchers (e.g., Yarkoni et al.)
Big Five Facets
30
Liberalism Imagination
Using correlations with LIWC features as reported by previous researchers (e.g., Yarkoni et al.)
Modeling Personality
Research in psychology have shown that word usage in ones writings such as blogs and essays is related with ones personality
Our research has correlated Big 5 facet features with willingness and readiness to respond to questions on social networks
Industry Solutions Joint Program
Ongoing: Correlating Brands with Personalities
Data
3000 Twitter users discussing Walmart, Costco, and Sears
Analytics
Measured personality traits from twits
Openness scores per brand shown to right.
All Brands
Costco
Sears
Walmart
Brand Perceptions from Twitter Data
Twitter Data
Collected from Twitter4J Stream-fashion API
Queries: Walmart, Costco, Sears
Total ~1M tweets , from May 31st- June 21 (3 weeks)
Data Filtering & Enhancements
Started with 6 categories from Consumer Report, selection, quality, layout, service, checkout, value
Category expansion- using synonyms, Wordnet and related terms. E.g.,
Added new categories that POP out of the data, jobs, people, visiting, experience...
E.g., I don"t know why I even try anymore. Walmart always disappoints. #walmartsucks
Walmart stay crowded!
checkout
line,wait,cashier,counter,self checkout,cash,register
layout
Isle,passage,lane,shelf,door,gate,lost,spacious,narrow,row
Classification- Inputs
Training Data
Manually categorized ~3500 tweets
Walmart (1341), Costco (1420), Sears (1109)
Run WEKA with 3 different models
SVM
Multinomial
KNN
Run ICM with some tuning
Nave based
Knowledge based and rules based
Cross validation with 2-folds.
Data Expansion
Run algorithm that finds similar tweets for those 3500 categorized tweets, ended up with ~75,000 tweets.
Classification- Results
Walmart
Costco
WEKA
ICM
WEKA
ICM
Category
#tweets
Precision
Recall
Precision
Recall
#tweets
Precision
Recall
Precision
Recall
Visiting
112
0.313
0.357
0.412
0.5
244
0.457
0.496
0.86
0.39
Jobs
187
0.907
0.834
1
0.782
32
0.773
0.531
1
0.538
Experience
99
0.253
0.222
0.272
0.312
114
0.199
0.254
0.177
0.544
Checkout
135
0.927
0.748
0.963
0.791
16
0.333
0.188
0.2
0.12
Service
17
0
0
0
0
3
0
0
0
0
Value
19
0.5
0.053
0
0
53
0.1
0.057
0.125
0.08
Layout
6
0
0
0
0
4
0
0
0
0
Quality
18
0.25
0.056
0
0
53
0.16
0.075
0.2
0.318
People
81
0.294
0.185
0.357
0.256
43
0.053
0.023
0.2
0.3889
Pharmacy
63
0.925
0.778
0.92
0.741
95
0.926
0.916
0.905
0.941
Selection
50
0.389
0.14
0.112
0.409
132
0.284
0.189
0.407
0.159
Other Category
574
0.598
0.763
0.673
0.111
712
0.625
0.704
0.702
0.5938
Visiting,112OtherCategory,574Jobs,187Experience,99Checkout,135Value,19Service,17Layout,6Quality,18People,81Pharmacy,63Selection,50
Classification- Insights
Insufficient evidence: some categories are rarely discussed in the tweets. E.g., layout, service.
Complexity of category: Even for the same number of training data, the classification for some categories have much lower accuracy
Infrastructure- UI
Work in progress
Improve classification quality of brand perceptionsUnsupervised methods to uncover unknown perceptions
Relating brand perceptions with consumer personalities
User studies on marketing professionalsHow well the tools support marketing tasks?
Qcrowd: asking targeted strangers questions
Engagement Continuum
System Architecture
Research Questions
Where might this be helpful?Questions about an event that are best answeredsoon after the event
Questions for which there might be a diversity of opinions
More?
How feasible is this approach?Will people answer questions from strangers?
Will use of incentives increase responses?
What is the quality of the answers?
Test Scenarios: TSA Tracker & Camera Review
Crowdsourcing airport security wait time via twitter
Crowdsourcing product reviews via twitterAsk follow-up questions if responded
Questions Asked
TSA TrackerWithout incentive
With incentive
Camera Reviews
Results
Follow-up Questions
Observations
Thank You! Questions?
User System and Experience Research (USER)IBM Research Almadenhttp://www.almaden.ibm.com/cs/disciplines/user/
Jilin Chen
Allen Cypher
Eben Haber
Eser Kandogan
Tessa Lau
Jalal Mahmud
Jeffrey Nichols
Barton Smith
Huahai Yang
Michelle X. Zhou
Tara Mathews
IBM Research - Almaden
2012 IBM Corporation
IBM Research - Almaden
2012 IBM Corporation
2011 IBM Corporation
IBM Research Haifa
IBM Research - Almaden
2012 IBM Corporation