‫Now, Amazon Comprehend
‫is a very simple service from an exam perspective.
‫All you need to know is that Comprehend is
‫to comprehend stuff,
‫so it is for Natural Language Processing, or NLP.
‫So anytime you will see NLP on the exam,
‫you would think Amazon Comprehend.
‫Now what it is, it is a fully managed and serverless service
‫and it will use machine learning to find insights
‫and relationships in your text.
‫For example, it can understand what is the language
‫of the text, it can extract key phrases,
‫places, people, brands, or events from that text.
‫It can understand, do a sentiment analysis to understand
‫how positive or negative the text you're analyzing is.
‫It can also analyze a text using tokenization
‫and parts of speech.
‫It also has audio.
‫And finally, it can organize a collection
‫of text files by topic, and find topics itself.
‫So, Comprehend is really about getting a lot of data in
‫and then Comprehend will do the rest
‫to try to understand the meaning of that data.
‫So it's about taking text or unstructured data
‫and structuring it around these features.
‫So some sample use cases of Natural Language Processing,
‫or NLP, are for example, to analyze customer interaction.
‫So say you have a bunch of customers sending you emails
‫and you want to understand overall based
‫on your support service what leads to a positive
‫or negative experience from your customers.
‫So we use Comprehend to extract these features
‫and then you will have the business insight
‫and then you can improve your business,
‫thanks to that analysis.
‫You can also, for example, create
‫and group articles by topics that Comprehend will uncover.
‫So imagine you have a lot of articles
‫and you want to group them together instead
‫of going to them one by one, you would feed them
‫into Comprehend and then it would output you
‫the topics you need to group them by.
‫So that's it.
‫So again, a bit more information than what you need,
‫but Comprehend from an exam perspective is
‫for Natural Language Processing.
‫So hope you liked it,
‫and I will see you in the next lecture.