Wednesday, April 17, 2013

Text  Mining on F-word

I have a colleague who works as the analytical practitioner and recently she was involved in banking project where they were analyzing free text data collected online.

The idea was to hear who is talking out there about this company,  what are they saying, how influential are the voices, what is the sentiment, what is the critical mass ad so on. And no better words to start your exploration of negative sentiment than F-word, and then go on from there.

Next thing my colleague had done - was to use a technique called concept linking which takes selected word, in this case F-word, and produce a graphical display of the linkages between that word and other entities. And the thicker links would indicate a stronger connection between the words.

So, there she was, sitting with a senior bank manager who was probably dressed in a grey suit and tie,  using some neat technologies for linguistic exploration to find the most F–ed up areas of the business.  Isn't this just pure pragmatism! Basically - let’s see what are the customers most angry about before we see  if we can do something about it.
Next time someone throws expletive in your face – don’t get angry, try to learn from it!  
Goran Dragosavac

1 comment:

  1. Text mining is a relatively new term for those outside the realm of the technical sphere. To lay it out in simple layman’s terms, it is precisely a process that finds its parallel in text analysis. Mining, as is common knowledge, is the act of extraction, and adding text to that, makes it a method of deriving high-quality text from the Web