This chapter covers
- An overview of natural language processing
- (NLP)
- How to approach an NLP machine learning scenario
- How to prepare data for an NLP scenario
- SageMaker’s text analytics engine, BlazingText
- How to interpret BlazingText results
Naomi heads up an IT team that handles customer support tickets for a number of companies. A customer sends a tweet to a Twitter account, and Naomi’s team replies with a resolution or a request for further information. A large percentage of the tweets can be handled by sending links to information that helps customers resolve their issues. But about a quarter of the responses are to people who need more help than that. They need to feel they’ve been heard and tend to get very cranky if they don’t. These are the customers that, with the right intervention, become the strongest advocates; with the wrong intervention, they become the loudest detractors. Naomi wants to know who these customers are as early as possible so that her support team can intervene in the right way.
She and her team have spent the past few years automating responses to the most common queries and manually escalating the queries that must be handled by a person. Naomi wants to build a triage system that reviews each request as it comes in to determine whether the response should be automatic or should be handed off to a person.