What Is NLP?
Essentially, natural language processing is a subfield of AI and linguistics concerned with how computers process human language. It covers areas such as:
- Sentiment analysis (how is the speaker/writer feeling – positive, negative, neutral?)
- Emotion detection (are they happy/angry?)
- Theme detection (what is the conversation specifically about?)
- Intent (why is the customer contacting the business?)
Linguistics (or rule-based techniques) consist of creating a set of rules and grammars that identify and understand phrases and relationships among words. These are developed by linguistic experts and are then deployed on the NLP platform.
NLP in Action
Take this example. “The branch staff were very helpful but the interest rates on your loans are too high, so I will go elsewhere.”
This is a complex sentence with positive and negative comments, along with a churn risk. Using NLP enables you to go beyond the positives/negatives to understand in detail what the positive actually is (helpful staff) and that the negative was that loan rates were too high.
Both of these precise insights can be used to take meaningful action, rather than only being able to say X% of customers were positive or Y% were negative.
If, instead of NLP, the tool you use is based on a “bag of words” or a simplistic sentence-level scoring approach, you will, at best, detect one positive item and one negative as well as the churn risk.
The issue is that, when it comes to a root-cause analysis, your tool’s insight will give the cause of churn as “staff experience and interest rates”. That’s why accuracy matters. You need a high level of precision and a tool with the ability to separate and individually analyse each unique aspect of the sentence.
Natural language processing has two main subsets – natural language understanding (NLU) and natural language generation (NLG).
As the names suggest, NLU focuses on understanding human language at scale, while NLG generates text based on the language it processes. This could mean reading a range of documents and creating a summary of them that is intelligible and useful to humans.
Thanks to Enghouse Interactive
