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Predictive Semantic Social Media Analysis David A. Ostrowski

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.

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Predictive Semantic Social Media Analysis David A. Ostrowski

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  1. Predictive Semantic Social Media Analysis David A. Ostrowski System Analytics and Environmental Sciences Research and Advanced Engineering Ford Motor Company

  2. Social media • Influential • Sample of the web • News driven • CRM • Real-time • Less biased • Unique opportunities for analytics

  3. Opportunities • Old Model • Reactionary • Damage control • Inquiries • Confirm positive reaction • New Model • Preemptive • Focused engagement • Promotions • Events • Media • Anticipatory

  4. Social Dimensions • Describes affiliations across a network • Values / Community • Reinforced by relationships • Utilize to predict purchase behavior

  5. Relational Learning • ‘Birds of a Feather’ • Leverage each local network to semantic understanding • Relational Learning =>Social dimensions

  6. Framework Overview • Relational learning • Strengthen representation • Support knowledge • Unsupervised classification • Generation of dimensions • Supervised classification • Dimensions => behavior

  7. Framework Overview Local network taxonomy Social Dimension labels Higher level features behaviors features features K-means cluster Supv. classification RN classification

  8. Case Study One • 4000 facebook identifiers • Associations to two vehicle lines • Question: • What can we extract to characterize between these two purchase behaviors

  9. Extracted data from FB Consolidated interests Applied the RN algorithm Guided by taxonomy Relational Learning Step

  10. Preliminary cluster statistics normalized differences between vehicle lines

  11. 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

  12. Applied Supervised Classification for the Behavior prediction • Applied sets through three Machine Learning algorithm • Simple Bayes • precision .7 , recall .69 • Weightily Averaged One-dependence Estimators • (WAODE) • precision .69 recall .70 • J48 • precision .69 recall .70

  13. 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

  14. Next Steps • Institutionalization • Extract / define exactly what our dimensions are explaining in our data sets. • Relate to specific association • Values • community

  15. Q/A See me for friends and neighbors discount…. dostrows@ford.com

  16. Appendix (software) • ‘R’ igraph • ‘R’ km module • Weka • Ruby -Watir

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