Hello everyone.
This is Pratap
and am the product manager for OCI language.
Today.
I'm going to show you how to use
OCI language
to analyze text
and visualize the output
in Oracle analytics without writing any code.
OCI a language offers apis for analyzing the text for various tasks, including
sentiment, named entity recognition and doing the text translation.
Many more tasks. Oracle analytics is a powerful
business intelligence platform
and offers out of the box integration with OCI language and OCI vision.
There are 3 simple steps which you need to follow for analyzing the text
and visualizing the output.
You need to create a connection with
the tenancy details
and then select the
text to be processed from the source data set,
and then select language models to be applied,
and then you're ready for creating the visualizations.
Let me show you quickly how to do that.
First you need to create connection details.
You need to configure the tenancy.
You need to configure the OCID,
and you have to configure the api key.
Once you create
the connection, then you have to register.
You have to register
the OCI
language models.
Now, for this connection, I am going to register
the sentiment analysis and named entity recognition.
Let me select the sentiment analysis here.
You need to configure the staging bucket.
This is the place
they output from the osha language
is stored.
So for here
I'm going to specify
the bucket
that's existing
and I click on register
to save the changes.
I'll repeat this step for
named entity recognition and click on register to save.
We are ready to analyze the texts.
Now I create a dataflow.
I specify the dataset here for this particular demo.
I have my review stored in spreadsheet, but in
real life the datasource can be anything.
It can be a database storing the
reviews, or it could be a Siebel system.
Having the review stored, I click on add
a review these
columns. Then I click on
the add step.
This is where I specify
what
model I want to apply.
Here. You will see the list of a model that were registered.
So I will select sentiment analysis here.
For sentiment analysis.
The output columns I leave, but
you need to specify the parameters.
Input column is the column where
actual texts to be analyzed resides.
For us, we are going to
analyze review texts.
I specify the review text
analysis level.
I want to apply
both aspect and sentence.
The reference column helps you to identify each column. So
reference column one I will select as review
ID, which is the unique ID for each review.
Then
you can select optimally 2 other columns, where
these column values will be copied to the output.
I will select review text so that I can
display the review text there.
With that, where we're done with the configuration of AI model,
the next step
is specifying
the save data.
I specify the dataset name here.
Hotel
review output.
Here I specify
the output dataset name, and here we can see the sample output that is produced.
For example, this particular review.
I need to ensure that these columns
are
properly configured
for positive score, negative score, the aggregate value as specified as average.
And with that
I'm done
with configuring the data flow.
I click on save
After that
I run this data of flow.
Once the
data flow is run,
we can create beautiful visualizations
using simple drag-and-drop.
Let me create one here.
I want to create a map.
We can see
the
map visualization,
and I want to specify the size of
the dots here, based on the number of reviews that are posted,
so that we can see which
region has posted maximum number of reviews
we can see here.
This is the state
which has received maximum number of
reviews.
Here are some other visualizations
which can be produced
from sentiment analysis.
This shows the number of reviews, a number of aspects identified.
This is one more
chart which shows the trend
here. It shows from July
till August
last year,
there are significant number of positive reviews, but suddenly
there is a drop, whereas there is a consistent
number of
negative reviews that are posted here.
And
we can also see the positive score versus negative
score express towards individual hotels here.
And this is one more visualization which helps depicting the trend.
Is there any seasonality of the positive or negative trend? Generally,
you can see here the negativity
goes up
during Friday and Saturday,
and this is one more visualization which shows the negative versus positive
trend. Here.
It shows the room
got most negative
sentiment,
whereas
the staff
has got the most positive
sentiment. And this is one more.
The business owner wants to understand what exactly is
the aspect that is contributing to negative vacant.
Build the text, highlighter visualization here.
People are happy because of the desk clerks.
This is one more visualization to
display
sentiment results on a map.
In this video, I have shown how Oracle analytics out of the box integration with
OCI, can be used to generate powerful insights and
appealing visualization without writing any code.
Thank you for watching.