Natural language processing on customer note data
This work addresses the need for faster analysis of sensitive B2B data for businesses, but it is incremental as it applies existing NLP methods to a new domain.
The paper tackled the problem of automatically analyzing large B2B customer note data by applying sentiment analysis, topic modeling, and keyword extraction, showing that accurate sentiment can be extracted and notes can be sorted by relevance into topics, though topics may lack business relevance without clear separation.
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.