Churnbar tweet classification
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Case Study: social search, sentiment analysis, and classification, using Churnbar tools (http:://churnbar.com)
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WeTestAthens: Postman's AI & Automation Techniques
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Churnbar tweet classification
- 1. ChurnBar
- 3. Churn: 10.3%
- 5. £450.7m
- 8. Manual Classification: existing systems
- 9. Bayesian Classifier or independent probability model: to be developed.
- 10. Link to CRM system – to be developed. We will need to work with a CRM operator in order to test the system with real data.
- 12. Used our unique social media listening tool to collect posts;
- 13. Only used Microblogs (twitter and Friendfeed);
- 14. 1 week – 19th – 25th July;
- 15. Classifier
- 16. 200 randomly selected posts classified as positive, neutral, or negative – used to train classifier;
- 18. “@MarpleLeafPoynton residents campaigned fiercly to stop Tesco & won.They weren't anywhere near…”
- 19. “RT @Scotty_McTweety : note to self, tesco value plastic spoons are not designed for stirring”
- 20. 1541 positive; 422 high confidence
- 21. “@ Dan_Phillips93 oh :( Yeah, well I enjoy having to get permission to sell alcohol in tesco ...”
- 23. “ASDA CHOSEN BY YOU DEEP FILL BACON QUICHE ... Doesn’t actually taste that good. Stop”
- 24. “@asda why hasn't my never say never dvd fans directors cut been delivered to my house yet??”
- 25. 813 positive; 126 high confidence
- 26. “@JaidakissLOOOLsooooo.... i think we needa go asdaim start dancing agen :p ima bad gyal dancer”
- 28. “@ iChazzer I was going to get nesquik powder from sainsburys but thought it was rather expensive “
- 29. “in sainsburys while with my gran. Basterd”
- 30. 1491 positive, 626 high confidence
- 31. “@LouiseStewart No because the middle class shop at quality supermarkets like Sainsbury’s”
- 33. Sainsbury’s posts classified to call / no call – results offer obvious actions.
- 34. Criteria can be: a complaint; an insult - burning down the store; a problem; or a query... not always –ve.
- 36. “Had two mishaps at Sainsburys. Pack of Carling collapsed and damaged my little finger.Ouch! Also £6…”
- 37. “@abikaylicious but my own always has kokojare. Hmmm. Do they sell poundo flour at sainsburys ? I…”
- 39. Using our tools, we can then find out more about the person.
- 44. Local issues
- 46. 1000 texts per supermarket per day (PSPD)
- 47. With 10 action items PSPD
- 48. Manually classify: (2 microblogs only)
- 49. 60 sec per text .
- 50. 60k sec (20 hours) PSPD.
- 51. Charge out at £40 per hour: £800 PSPD.
- 53. Automation will reduce costs. People costs are focussed on customer experience.
- 54. Drivers for multiple platforms (Facebook etc.) future-proofs investment
- 55. Make this activity practical (reading these texts is mind numbing) for volume brands with 1:100 signal to noise.
- 56. Sentiment classifiers make the system scalable.