predictive semantic social media analysis david a. ostrowski
DESCRIPTION
Predictive Semantic Social Media Analysis David A. Ostrowski System Analytics and Environmental Sciences Research and Advanced Engineering Ford Motor Company. Social media. Influential Sample of the web News driven CRM Real-time Less biased Unique opportunities for analytics. - PowerPoint PPT PresentationTRANSCRIPT
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Predictive Semantic Social Media Analysis
David A. Ostrowski System Analytics and Environmental Sciences
Research and Advanced Engineering
Ford Motor Company
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Social media
• Influential• Sample of the web
– News driven• CRM
– Real-time– Less biased
• Unique opportunities for analytics
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Opportunities
• Old Model– Reactionary
• Damage control• Inquiries• Confirm positive reaction
• New Model– Preemptive
• Focused engagement– Promotions– Events– Media
• Anticipatory
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Social Dimensions
• Describes affiliations across a network
• Values / Community
• Reinforced by relationships
• Utilize to predict purchase behavior
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Relational Learning
• ‘Birds of a Feather’
• Leverage each local network to semantic understanding
• Relational Learning =>Social dimensions
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Framework Overview
• Relational learning– Strengthen representation– Support knowledge
• Unsupervised classification– Generation of dimensions
• Supervised classification– Dimensions => behavior
Movies Television Shows associationsschools
Fb identifier Fb identifier Fb identifier
Political affiliations Issues positions
values
Buying habits
Religious views
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Framework Overview
Localnetwork
taxonomylabels
SocialDimension
RNclassification
K-meanscluster
features
Supv.classification
behaviorsfeatures
Higher level features
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Case Study One
• 4000 facebook identifiers
• Associations to two vehicle lines
• Question:– What can we extract to characterize between these
two purchase behaviors
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Relational Learning Step
• Extracted data from FB
• Consolidated interests
• Applied the RN algorithm
• Guided by taxonomy
45 50 55 60 65 70 75 80 85 90
0
10
20
30
40
50
60
70
80
90
100
Facebook Accounts
missing labels (normalized)
Acc
ura
cy
RNBayesk-Means
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Preliminary cluster statistics
1 2 3 4 5 6veh1 k=3 46 39 13veh2 k=3 21 42 36veh1 k=4 44 16 12 26veh2 k=4 14 27 24 32veh1 k=5 21 8 1 0.3 45veh2 k=5 35 22 12 15 14veh1 k=6 7 43 6 13 9 19veh2 k=6 20 14 16 8 9 35
normalized differences between vehicle lines
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Extracted social dimensions
• Applied feature sets to k-means (3-6)
• Each classification attempt to characterize between vehicle line and a social dimension (value / interest ..)
• All classification to be considered towards behavioral training
• Also considered community detection– Via maximization of a modularity matrix via leading eigenvectors
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Applied Supervised Classification for the Behavior prediction
•Applied sets through three Machine Learning algorithm
•Simple Bayesprecision .7 , recall .69
• Weightily Averaged One-dependence Estimators(WAODE)precision .69 recall .70
•J48precision .69 recall .70
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Case Study 2
• 20000 Facebook IDs across four vehicle lines
• Relational modeling– Similar performance as first case study
• Social Dimensions generated for k=(3-7)– Not as much separation after k=6 clustering
• Precision recall (among simple bayes, WAODE, J48).469, .483.591, .588.534, .536
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Next Steps
• Institutionalization– Extract / define exactly what our dimensions are
explaining in our data sets.
• Relate to specific association – Values– community
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Q/ASee me for friends and neighbors discount…. [email protected]
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Appendix (software)
• ‘R’ igraph• ‘R’ km module• Weka• Ruby -Watir