How simple should Text Analytics be?

(Playing with Semantria.com)

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.

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

Semantria’s key benefits...cont.

5. It is simple to use – even I understood it.

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.

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

‘amplifi ers’ (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 Twitter because it’s a short communication; ignore 1 for longer

document types)

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/specifi c needs

* searched 7TB of Wikipedia to build a giant thesaurus at the heart of the

engine…it knows ‘Coca Cola’ is a beverage, closely related to vodka, not

at all related to shoes…

The ‘Collection’ option allows users to dive into the problems identified in the first stage

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.

I’m off to try it now…

*10,000 API calls as part of a free trial.

I’ll let you know how it goes…

Appendices

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.uk

As 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