Extraction de Contexte et la Signification des Médias Sociaux

1. Extracting meaning from Social Media Monitoringor… 19/11/2010 Keith Woods-Holder @socialitekwh

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3. The tools used often present you with this…

6. Try a bottom up approach to define filters – it’s quicker and it will help understand the nature of the social media and how your brand interacts with it better – particularly useful when you need to ‘expand’ the search

7. Use as many specifics as you can (that may be none, but usually isn’t)

8. Be prepared to spend some time planning how you will use filters to categorize and quantify data (every hour you spend at the planning stage can save you days in execution – and can be especially valuable when you are briefing vendors)

9. Make sure the filters make sense to your business and monitoring objectives 5

10. The perfect tool? 6

12. Better still, choose a tool which allows you to check your assumptions

13. Experiment and test your assumptions before you go setting up a whole lot of details in specifications and filtering

14. Use ‘exceptions’ to exclude unwanted audiences

15. Make sure you understand the vocabulary your audiences use7

16. Data cleanup

17. Customizing the sample 9

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21. A matter of perspective

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24. Context 16

26. Either make sure you test ‘logical’ questions ahead of timeor… 17

28. The benefit is that missed ‘hits’ are minimised simply because your vocabulary missed a word or variation, or the time tense is different, or one or more of the key elements isn’t language (such as an emoticon, or slang expressions)

29. This approach allows you to work in a more natural way in both framing questions and evaluating outcomes18

31. If you have designed in the filtering, contextual mapping and the end user of the information it is perfectly practical to expect a result in the 90-95% accuracy range

32. Beware of claims over 95% (in a ‘live’ language, subject to fashion and the re-use of terms in new ways there are very real and tangible barriers

33. But don’t make the mistake of assuming a human reader can do any better – a 2009 study of 2,000 people saw them score an average of 84% – and 88% was the top mark19

35. Black box solutions, however good they appear, are hiding things from you – insist on transparency 20

36. Death to all humans? 21

38. Take the Apple iPhone 4 – social media DISCUSSION THREADS not posts were ruining at 30/second during the launch day – leaving anyone trying to read and make sense of the sentiment in a passive, or reactive state.

42. Ability to dynamically handle high volume up to 10,000 per hour

43. Sentiment Analysis is computer generated using Natural Language Processing (NLP),able to score slang, irony and even symbolic language 

44. Accuracy levels are high, transactions to provide overall article scoring with ‘transparent’ sentiment scoring.

45. Use of anaphora to identify “its”, “their”, “the company” and maintain reference

46. Fully integrated with all other media types – no silos – full cause and effect mappingBusiness Drivers • Social media multiplies media outlets into millions• Business reaction time reduced to real-time responses• MarCom and press office merges Deliverables • Fully interactive 'right time' delivery of filtered and categorised materials • Innovation in removal of 'Black Box' allows brands to obtain 'best fit' results 2010 Tool providers include: Glide Technologies

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