  Automatic analysis of customer data for businesses is an area that is of
interest to companies. Business to business data is studied rarely in academia
due to the sensitive nature of such information. Applying natural language
processing can speed up the analysis of prohibitively large sets of data. This
paper addresses this subject and applies sentiment analysis, topic modelling
and keyword extraction to a B2B data set. We show that accurate sentiment can
be extracted from the notes automatically and the notes can be sorted by
relevance into different topics. We see that without clear separation topics
can lack relevance to a business context.
