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
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
Use as many specifics as you can (that may be none, but usually isn’t)
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)
Make sure the filters make sense to your business and monitoring objectives
Look at the new generation of analyzers which combine natural language and context to generate results which ‘mean’ what you ‘said’ in your question
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)
This approach allows you to work in a more natural way in both framing questions and evaluating outcomes
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!
Black box solutions, however good they appear, are hiding things from you – insist on transparency
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
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.
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.
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!
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
Ability to dynamically handle high volume up to 10,000 per hour
Sentiment Analysis is computer generated using Natural Language Processing (NLP),able to score slang, irony and even symbolic language
Accuracy levels are high, transactions to provide overall article scoring with ‘transparent’ sentiment scoring.
Use of anaphora to identify “its”, “their”, “the company” and maintain reference
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