Extracting Context

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Extracting Context

  1. 1. 01/10/2010<br />Extracting meaning from Social Media Monitoring<br />….OR<br />
  2. 2. 01/10/2010<br />2<br />
  3. 3. Is they often present you with this...<br />01/10/2010<br />3<br />
  4. 4. Understanding the meaning of results and how to use monitoring tools so they work for you <br />Filtering the information<br />Using a top down approach to filtering often wastes time, money and effort<br />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<br />Use as many specifics as you can ( that may be none, but usually isn’t)<br />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)<br />Make sure the filters make sense to your business and monitoring objectives <br />01/10/2010<br />4<br />
  5. 5. 01/10/2010<br />5<br />The perfect tool?<br />
  6. 6. How much do you already know about your audience?<br />Take the time to establish which social media channels it makes sense for your audience to use <br />Better still, choose a tool which allows you to check your assumptions<br />Experiment and test your assumptions before you go setting up a whole lot of details in specifications and filtering<br />Use ‘exceptions’ to exclude unwanted audiences<br />Make sure you understand the vocabulary your audiences use <br />01/10/2010<br />6<br />
  7. 7. 01/10/2010<br />7<br />Data Cleanup<br />The problem with social media is that finding 20,000 ‘hits’ is a lot easier than finding the 50 that matter<br />Filters and exclusions can screen out obvious mismatches but can also cause you to ‘miss’ important items<br />A successful strategy for cleaning data without losing integrity should include:<br />Contextual matches rather than keywords<br />Non-linguistic cues and vocabulary<br />Links and associations (especially where you can set limits/minimum values)<br />Use of active terms <br />Use of specialized tags and terms to allow easier identification of who is responding to your brands content<br />Incorporation of SEO terms used in materials designed for audiences<br />Links outside social media groups for source and destination tagging<br />
  8. 8. Customizing the sample<br />The first rule of data sampling should be: design backwards from the intended result/audience for your results”<br />Establish what you are going to do with results <br />Who are the ultimate recipients<br />Do results have be consistent with other business functions’ results<br />Don’t generate information no one can use ( i.e. if you are monitoring in real time - make sure there are real time processes which uses these results<br />Question outside your own immediate needs – are you monitoring for research, evaluation, discovery, actionable insight, immediate response?<br />Make sure your ‘cleaned data’ can be transformed into all the outlets and reports you require<br />01/10/2010<br />8<br />
  9. 9. A matter of Perspective<br />Perspectives are a quick and easy way of overcoming one of the most persistent problems with analyzers - making the answer relevant to you.<br />In data cleanup they can be used to eliminate a lot of irrelevant content<br />In research they can be used to shift the view between , say, competing brands or topics<br />Most analysers use people to add the perspective....<br />...but there is an alternative<br />Automated perspectives can be specified as part of the filtering and cleanup process<br />‘stored perspectives’ allow a rapid comparison of data from different viewpoints which multiplies the business/media intelligence in the sample <br />Anything automated can be switched on or off and can be modified<br />Consistency in results will go up<br />01/10/2010<br />9<br />
  10. 10. Context<br />Most sentiment analysers do not allow or imply context in their results<br /> which is why the results often don’t look right <br />Context uses perspectives and filters to provide a means of matching the results to the brand or person relating to the monitoring objective<br /> Put simply - the results should make sense <br />Context also allows the focus of monitoring to reflect actual process in a business e.g. A ‘buy’ decision or an expression of discontent about a service<br />The role of context is to make ‘You’ the focus of the answer<br />01/10/2010<br />10<br />
  11. 11. Framing Questions<br />The most important rule is to make sure the answers are answering YOUR questions <br />Either make sure you test ‘logical’ questions ahead of time <br /> or......<br />01/10/2010<br />11<br />
  12. 12. An end to Keywords?<br />Look at the new generation of analyzers which combine natural language and context to generate results which ‘mean’ what you ‘said’ in your question<br />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<br />This approach allows you to work in a more natural way in both framing questions and evaluating outcomes<br />01/10/2010<br />12<br />
  13. 13. The trouble with automated sentiment analysis<br />Is often what it is expected to accomplish<br />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<br />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<br />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<br />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!<br />Black box solutions, however good they appear, are hiding things from you - insist on transparency <br />01/10/2010<br />13<br />
  14. 14. Death to all humans?<br />01/10/2010<br />14<br />
  15. 15. Why automated analysis is NOT about replacing people<br />The role of automation is not about replacing people from the process – rather is should be about allowing you more time to think about what is important rather than speed reading<br />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.<br />This is an extreme, but most social media sites can easily overwhelm a human-based system allowing people to do little more than skim content<br />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.<br />01/10/2010<br />15<br />
  16. 16. Making results count<br />AVE? ROI? If you are going to be subject to externally generated measures (often for historical or consistency reasons) make sure you can measure something meaningful for the purpose<br />Better still develop a metric which actually makes sense for what you are doing and communicate its benefits<br />Make sure your results are not siloed to just social media - there are often links to other media forms (including your own materials) – make sure your solution integrates them all and can show you these relationships<br />Dashboards:<br />Employ ways of making your results and their context stand out – dashboards are both an excellent way of compressing complex information and adding impact to results (but too often are generalized templates presented by vendors – make sure you get what you want – how you want it)<br />Can be used to extend your information into other parts of the business<br />Can create synergy with other business processes - make sure your social media dashboard can take on information from other functions<br />Don’t misuse dashboards to create meaningless measurements or introduce concepts which make no sense to anyone other than the graphics/metrics person who designed it - stick to what you know and can understand<br />01/10/2010<br />16<br />
  17. 17. And finally....<br />Feedback loops – both in the human and automated sense are a valuable mechanism to ‘shrink wrap’ results ever closer to you brand objectives - use them!<br />01/10/2010<br />17<br />
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