There are plenty of systems out there that record notes about contacts between customers and suppliers (students and providers in our context), but hardly any of them is able to extract from those notes how the student is probably feeling.  Not just how they are feeling in general about things but specifically, how they are feeling about the various strands of their lives, their learning and the plethora of things that make up our sentiments as they change from day to day.

​

What SAM (Sentiment Analysis Manager) allows you to do is to use data from engagements between you and your students, regardless of the channel it comes through and regardless of whether it is structured or unstructured, in order to understand how each student is feeling.

​

Structured sentiment analysis relies on the creation of mappings between questions and answers that may be asked and given during phone calls, email exchanges, micro-surveys, chat etc.  Those mappings allow you to set up as many sentiment subjects as you wish in as many groupings as you wish and then to place scores that indicate a positive, ambivalent or negative sentiment about any aspect of the engagement.

​

Unstructured analysis allows you to extract sentiment (plus many other aspects of personality) based on natural language processing (NLP).  For example, any free text notes, email content, chat content and ever discussion content from your LMS can be used by SAM to extract sentiment and add further texture and insight to the more formalised structured analysis approach.

​

​

​

​

​