[rsmith49]

Transfer learning is the premise of learning from data in one domain, and then being able to transfer that knowledge to another domain. In this case, we leverage large existing text datasets to build an initial model, and then tailor that model to customer feedback and apply it to our specific problem (for this blog post, classifying feedback sentiment).

We chose to use this approach for customer feedback since it combines the benefits of a general model (large amounts of data) with the benefits of a domain-specific model (targeted to customer feedback). It looks like we were validated, as it outperformed Google NLP API and AWS Comprehend - both general models - on our set of customer feedback data.