Year: 2023
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Hilditch, Andrew, Webb, David, Baca, Jozef, Armitage, Tom, Shardlow, Matthew, Appleby, Peter
Institution: Manchester Metropolitan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4368755250