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Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
Glide Technologies - Keith Woods-Holder
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Glide Technologies - Keith Woods-Holder

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Extraction de Contexte et la Signification des Médias Sociaux

Extraction de Contexte et la Signification des Médias Sociaux

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  • 1. Extracting meaning from Social Media Monitoringor…<br />19/11/2010<br />Keith Woods-Holder<br />@socialitekwh<br />
  • 2. 2<br />
  • 3. The tools used often present you with this…<br />
  • 4.
  • 5. Understanding the meaning of results and how to use monitoring tools so they work for you <br />Filtering the information<br /><ul><li>Using a top down approach to filtering often wastes time, money and effort
  • 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 </li></ul>5<br />
  • 10. The perfect tool?<br />6<br />
  • 11. How much do you already know about your audience?<br /><ul><li>Take the time to establish which social media channels it makes sense for your audience to use
  • 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 use</li></ul>7<br />
  • 16. Data cleanup<br />
  • 17. Customizing the sample<br />9<br />
  • 18. 10<br />
  • 19. 11<br />
  • 20. 12<br />
  • 21. A matter of perspective<br />
  • 22. 14<br />
  • 23. 15<br />
  • 24. Context<br />16<br />
  • 25. Framing questions<br /><ul><li>The most important rule is to make sure the answers are answering YOUR questions
  • 26. Either make sure you test ‘logical’ questions ahead of time</li></ul>or…<br />17<br />
  • 27. An end to keywords?<br /><ul><li>Look at the new generation of analyzers which combine natural language and context to generate results which ‘mean’ what you ‘said’ in your question
  • 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 outcomes</li></ul>18<br />
  • 30. The trouble with automated sentiment analysis<br /><ul><li>Is often what it is expected to accomplish
  • 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 mark</li></ul>19<br />
  • 34. The trouble with automated sentiment analysis<br /><ul><li>There is a ‘semantic’ trap’ in most analysers in that they use language as it is defined – not as it is actually used – and most rely on words (social media posters sometimes don’t) – a lot of language isn’t traditional linguistics!
  • 35. Black box solutions, however good they appear, are hiding things from you – insist on transparency </li></ul>20<br />
  • 36. Death to all humans?<br />21<br />
  • 37. Why automated analysis is NOT about replacing people<br /><ul><li>The role of automation is not about replacing people from the process – rather it should be about allowing you more time to think about what is important rather than speed reading
  • 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.
  • 39. Context-based analysis can reduce the results to actions and insights which are both manageable and insightful – without increasing the errors from volume – without getting tired and it will read every blog or post all the way through.</li></li></ul><li>Making results count<br />23<br />
  • 40. And finally…<br /><ul><li>Feedback loops – both in the human and automated sense are a valuable mechanism to ‘shrink wrap’ results ever closer to your brand objectives – use them!</li></ul>24<br />
  • 41. 3rd Generation<br />4th Generation<br />2nd Generation<br />1st Generation<br />• Identification of keywords and text<br /> strings in context using Boolean logic for products, names and keywords <br />• Automated sentiment for<br /> keywords/strings using dictionaries <br />• Article level values but based<br /> on keyword / string values<br /> Automated identification of <br /> keywords Manual sentiment mark-up<br /> with article level values Simple totals positive, neutral,<br /> negative<br /> Identification of keywords and<br /> Boolean logic for product,<br /> names and keywords Automated sentiment for<br /> keywords using dictionaries<br />• Natural language tracking for<br /> phrases, names and equivalents (antonyms and synonyms) in contact entity<br />• 'Phrase level' analysis including anaphora.<br /><ul><li>We don’t use humans for analysis or dictionaries
  • 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 mapping</li></ul>Business Drivers<br />• Social media multiplies media outlets into millions• Business reaction time reduced to real-time responses• MarCom and press office merges<br />Deliverables<br />• Fully interactive 'right time' delivery of filtered and categorised materials<br />• Innovation in removal of 'Black Box' allows brands to obtain 'best fit' results<br />2010<br />Tool providers include:<br />Glide Technologies<br />
  • 47. 26<br />

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