Text analytics for verbal identity and branding (a first play with Semantria)


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A quick look at a simple text analytics package that we are examaning for suitability in our work with verbal identity.
"Quant in, magic out"

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  • Verbatims pulled into excel
  • Text analytics for verbal identity and branding (a first play with Semantria)

    1. 1. How simple should Text Analytics be? (Playing with Semantria.com)
    2. 2. Disclaimer: this Powerpoint document was written 5 minutes after I first saw a demonstration …I haven’t tested it myself yet to find its limitations. There are other text analytics providers out there.
    3. 3. Semantria’s key benefits. 1. Excel based* – everyone knows Excel. 2. It’s for SME’s, rather than Enterprise scale companies - -with a pricing model to match, (so even someone in a hurry in a large corporation can also get it signed off quickly. ) 3. Fast! - Processes up to 2000 docs/sec.† 4. Has Lexalytics as its engine – Radian6 etc use Lexalytics as their sentiment engine * Excel for Windows 2010 † We did 1000 tripadvisor comments quicker than a sneeze
    4. 4. Semantria’s key benefits...cont. 5. It is simple to use – even I understood it.
    5. 5. Procedure 1. Pull verbatims into Excel (see next slide) 2. Start analysis 3. Document mode 4. Select range 5. [processes the document] 6. Get the results… 7. Yep, that easy.
    6. 6. Notes 1. It shows multiple entries for single verbatims – because that’s the way it is 2. Document sentiment from a bank of 1.8 million sentiment phrases (e.g. good, v good) with ‘amplifiers’ (e.g. really + good)– logarithmic scale of -7 -> +7, 95% of results fall within -1 -> +1 3. Entity = people, places, companies, job titles, times etc . 4. Entity evidence – how many phrases are there to support that sentiment judgement* 5. Themes – noun-phrases which are important to the document and bear the most value to the theme of the sentiment * (1 = ok for Twi t t e r be ca use it’s a short communica t ion; ignore 1 for longe r docume nt type s)
    7. 7. Notes…cont. 7. Categorisation Engines built-in: so the user doesn’t need to train the software in the user ’s industry.* 8. Query – allows you to further personalise categories to suit the user ’s industry/specific needs * se a rche d 7 TB of Wi ki pe dia to build a gia nt the sa urus a t the he a rt of the e ngine …it knows ‘Coca Col a ’ i s a be ve ra ge , close ly re la te d to vodka , not a t a ll re la te d to shoe s…
    8. 8. The ‘Collection’ option allows users to diveinto the problems identified in the firststage 1. You have an overview from previous stage (e.g. ‘rude’ + ‘staff ’ seems to be coming up a lot). 2. Build a ‘Query’ that helps you identify which posts/ verbatims have this as a theme (e.g. look for verbatims where ‘rude’ occurs within 20 characters of ‘staff ’) 3. You can then either contact the customer who posted the verbatim and apologise; or refer the matter internally.
    9. 9. I’m off to try it now… *10,000 API calls as part of a free trial.
    10. 10. I’ll let you know how it goes…
    11. 11. Appendices
    12. 12. Verbal Identity is a brand consultancy specialising in language. We creates language which creates value for our clients. We work for brands in automotive, retail and telecoms. Find out more: www.verbalidentity.co.ukAs part of our approach to providing quantifiable solutions, we will work with a number of text analytics providers. We have no connection with Semantria.com