Hey, everyone.
And welcome back.
Now, in today's video, we are going to discuss about the Amazon comprehend service.
Now, before we go ahead and explore the service, let's take a simple use case to understand the benefits
that the service will provide.
So let's assume that there are 100 customer representatives that are working in a call center.
And based on the number of calls that have been received, all the conversations that is happening between
the representative and the customers, these conversations are automatically converted to text using
some form of a speech to text converter.
Now, based on this text based conversation that is recorded in the database, management wants to know
the overall sentiment of the conversation, whether customer was happy or it was a negative kind of
a sentiment and so on.
Now, in order to achieve such kind of a use case, you need some kind of a machine learning services
that can go through the text that was being recorded.
It can look into the keywords and based on which it can identify whether the customer was happy or he
was unhappy.
And in order to solve such use case, Amazon comprehensive service can be used.
So at a high level overview.
Comprehend is a natural language processing service that uses machine learning to uncover valuable insights
and connection in text.
For example, on the left hand side, you have certain texts that are available.
You feed in those text to the comprehend service.
Comprehensive service will go through these text and based on which it will perform some analysis related
to whether the customer was happy or unhappy, whether there was a positive or a negative sentiment,
whether there was any PII data that was shared as part of the text.
Pia is nothing but some kind of a personal data, maybe phone numbers, maybe debit, credit card information
and so on.
And it can also detect the language based on which the conversation has happened.
So this is the high level overview.
Let's go ahead and look into it practically post which we'll be discussing about few more other details.
So within the console, let's go ahead and quickly open up the comprehend service.
And from you, let's go ahead and quickly launch the comprehend now within.
One thing that you will see is in the input text, you can go ahead and specify the text.
This text is nothing but the one that is generated using some kind of a speech to text converter based
on the use case that we were discussing.
And based on this input text within the insights, you basically get various information related to
the entity.
In fact, for example, within this text, it was able to detect certain entities and the type of that
entity is person, organization, quantity it was able to detect some kind of a location was shared.
Similarly, as we were discussing about the sentiment here, it is basically detecting what is the sentiment,
whether it is neutral with certain level of confidence, positive, a certain level of confidence and
so on.
So for today's video, I have two sample reviews that are available.
I'll also be adding this after this video so that you can reference to it.
One is a positive review, second one is a negative review.
So let's go ahead and try it out.
We'll add it and we'll see what are the things that comprehend X, So I'll clear the text.
Let me add this review here.
I'll do an analyze and based on the analyze text, it is able to detect that there were certain entities
like John Smith, the type of entities person, and it was also able to detect certain location.
Next thing as we're discussing is the sentiment over here.
And in the sentiment here, you see it is stating positive with 0.999 confidence and other, which is
neutral, negative and mix is 0.00.
Along with that, it also checks for the language which is 0.98 English.
And it also verifies if there are any PII data that were shared.
Similarly, let's also copy the negative review.
Let's add it in the input text.
I'll do an analyze.
This time, let's look into the sentiment directly.
And here you see it is able to detect that it is a negative sentiment overall.
Great.
So I hope with this at a high level overview, you understood the basics of the comprehend service.
Now, along with this, the comprehend also comes with a feature of medical, so you can also refer
to it as an Amazon comprehend medical and it is a hyper eligible NLP service that primarily uses the
machine learning to extract the health data from the medical text, such as prescription procedures
or diagnosis.
So in case if there are any prescriptions given by the appropriate medical authority for those kind
of use cases, you can make use of the comprehend medical feature.
So earlier, the comprehend medical was also part of the Amazon comprehend service, but they have now
separated it out.
So you see they have a different service of comprehend medical.
So at this stage the Singapore region is not supported.
So let's quickly jump to the Virginia region where this service is available.
So let's go ahead and look into what the medical service does.
So there is a sample input text that is already available, and this input text contains various information
related to health care, including the name of medicines and various other details.
And within here you see the analysis has already been done where it was able to automatically detect
various dosage related information, various information related to the health of the patient and so
on.
So this is also quite interesting service in case health care related information and prescription information
needs to be analyzed.
So that's the high level overview about comprehend medical and with this will conclude today's video.