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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…
    19/11/2010
    Keith Woods-Holder
    @socialitekwh
  • 2. 2
  • 3. The tools used often present you with this…
  • 4.
  • 5. Understanding the meaning of results and how to use monitoring tools so they work for you
    Filtering the information
    • 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
    5
  • 10. The perfect tool?
    6
  • 11. How much do you already know about your audience?
    • 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
    7
  • 16. Data cleanup
  • 17. Customizing the sample
    9
  • 18. 10
  • 19. 11
  • 20. 12
  • 21. A matter of perspective
  • 22. 14
  • 23. 15
  • 24. Context
    16
  • 25. Framing questions
    • 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
    or…
    17
  • 27. An end to keywords?
    • 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
    18
  • 30. The trouble with automated sentiment analysis
    • 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
    19
  • 34. The trouble with automated sentiment analysis
    • 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
    20
  • 36. Death to all humans?
    21
  • 37. Why automated analysis is NOT about replacing people
    • 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.
  • Making results count
    23
  • 40. And finally…
    • 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!
    24
  • 41. 3rd Generation
    4th Generation
    2nd Generation
    1st Generation
    • Identification of keywords and text
    strings in context using Boolean logic for products, names and keywords
    • Automated sentiment for
    keywords/strings using dictionaries
    • Article level values but based
    on keyword / string values
     Automated identification of
    keywords Manual sentiment mark-up
    with article level values Simple totals positive, neutral,
    negative
     Identification of keywords and
    Boolean logic for product,
    names and keywords Automated sentiment for
    keywords using dictionaries
    • Natural language tracking for
    phrases, names and equivalents (antonyms and synonyms) in contact entity
    • 'Phrase level' analysis including anaphora.
    • 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
    Business 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
  • 47. 26

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