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A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings Nan Wang, Blesson Varghese Queens University Belfast Peter Donnelly University of Toronto 12-CRS-0106 REVISED 8 FEB 2013 1 10/26/16 Outline Motivation

  1. A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings Nan Wang, Blesson Varghese Queen’s University Belfast Peter Donnelly University of Toronto 12-CRS-0106 REVISED 8 FEB 2013 1 10/26/16

  2. Outline • Motivation • Methodology • Machine Learning Approaches • Case Study: Sandy Hook Elementary School Shooting • Limitations • Conclusion • Q & A 12-CRS-0106 REVISED 8 FEB 2013 2 10/26/16

  3. Outline • Motivation • Methodology • Machine Learning Approaches • Case Study: Sandy Hook Elementary School Shooting • Limitations • Conclusion • Q & A 12-CRS-0106 REVISED 8 FEB 2013 3 10/26/16

  4. A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings • Motivation – Apply and evaluate machine learning approaches for sentiment analysis on social network – Provide insights gathered from social networks to decision makers – Engage non-CS audiences with research outputs through interactive visualisation 12-CRS-0106 REVISED 8 FEB 2013 4 10/26/16

  5. Outline • Motivation & Contribution • Methodology • Machine Learning Approaches • Case Study: Sandy Hook Elementary School Shooting • Limitations • Conclusion • Q & A 12-CRS-0106 REVISED 8 FEB 2013 5 10/26/16

  6. 6 Methodology 10/26/16 12-CRS-0106 REVISED 8 FEB 2013

  7. Methodology • Pro-Gun Public Sentiment Score – Baseline – Correction for Volume of Tweets – Correction for Volume of Tweets & Population 12-CRS-0106 REVISED 8 FEB 2013 g: geographic region t: time frame 7 10/26/16

  8. Outline • Motivation & Contribution • Methodology • Machine Learning Approaches • Case Study: Sandy Hook Elementary School Shooting • Limitations • Conclusion • Q & A 12-CRS-0106 REVISED 8 FEB 2013 8 10/26/16

  9. Machine Learning Approaches • Feature Extraction – N-gram Uni-gram Bi-gram Tri-gram Not Not sure Not sure if sure sure if sure if gun if if gun if gun shot gun gun shot gun shot or shot shot or shot or fire or or firework or firework firework 12-CRS-0106 REVISED 8 FEB 2013 9 10/26/16

  10. Machine Learning Approaches • Feature Extraction – Hashtags #PrayForNewtown, #NRA, #guncontrol – Reply/Mention Tags @BarackObama, @Death, @cnnbrk 12-CRS-0106 REVISED 8 FEB 2013 10 10/26/16

  11. Machine Learning Approaches • Modelling – Support Vector Machine (SVM) – Naïve Bayes (NB) – Maximum Entropy (ME) – Decision Tree (Single, Bagged, Boosted) – Random Forest (RF) – Neural Network (NN) 12-CRS-0106 REVISED 8 FEB 2013 11 10/26/16

  12. Machine Learning Approaches • Evaluation 12-CRS-0106 REVISED 8 FEB 2013 12 10/26/16

  13. Machine Learning Approaches • Evaluation 12-CRS-0106 REVISED 8 FEB 2013 13 10/26/16

  14. Outline • Motivation & Contribution • Methodology • Machine Learning Approaches • Case Study: Sandy Hook Elementary School Shooting • Limitations • Conclusion • Q & A 12-CRS-0106 REVISED 8 FEB 2013 14 10/26/16

  15. Case Study: Sandy Hook Elementary School Shooting • Data Description – Timeframe Friday, 12/07/2012 00:00:01 GMT ~ Tuesday, 01/15/2013 23:59:59 GMT – Data Size 7 million tweets • Triple-class Sentiment – Positive “The only thing that stops a bad guy with a gun, is a good guy with a gun” – Negative “We NEED strict gun control. #Newtwon” 12-CRS-0106 REVISED 8 FEB 2013 – Neutral “Not sure if gun shot of firework.” 15 10/26/16

  16. Case Study: Sandy Hook Elementary School Shooting • Tweets Statistics 12-CRS-0106 REVISED 8 FEB 2013 16 10/26/16

  17. Case Study: Sandy Hook Elementary School Shooting • Tweets Statistics 12-CRS-0106 REVISED 8 FEB 2013 17 10/26/16

  18. Case Study: Sandy Hook Elementary School Shooting • Visualisation http://www.gunsontwitter.com/ – Motion Chart 12-CRS-0106 REVISED 8 FEB 2013 18 10/26/16

  19. Case Study: Sandy Hook Elementary School Shooting • Visualisation – Line Graph 12-CRS-0106 REVISED 8 FEB 2013 19 10/26/16

  20. Case Study: Sandy Hook Elementary School Shooting • Visualisation – Geo Map Baseline PGPSS 12-CRS-0106 REVISED 8 FEB 2013 12/07/2012 ~ 01/15/2013 12/13/2012 ~ 12/15/2012 20 10/26/16

  21. Outline • Motivation & Contribution • Methodology • Machine Learning Approaches • Case Study: Sandy Hook Elementary School Shooting • Limitations • Conclusion • Q & A 12-CRS-0106 REVISED 8 FEB 2013 21 10/26/16

  22. Limitations • Size of Twitter Corpus • Complexity of Feature Selection – emoticon – Part-Of-Speech tagging • Trade-off between Performance & Computing Power 12-CRS-0106 REVISED 8 FEB 2013 22 10/26/16

  23. Outline • Motivation & Contribution • Methodology • Machine Learning Approaches • Case Study: Sandy Hook Elementary School Shooting • Limitations • Conclusion • Q & A 12-CRS-0106 REVISED 8 FEB 2013 23 10/26/16

  24. Conclusion • This paper – Evaluates of machine learning approaches for twitter sentiment analysis – Investigates tweets’ relevance to gun violence – Visualises public sentiment related data on multiple geographic/temporal level interactively 12-CRS-0106 REVISED 8 FEB 2013 24 10/26/16

  25. 25 10/26/16 Q & A 12-CRS-0106 REVISED 8 FEB 2013

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