Thanks to AI Builder in Microsoft Power Apps and Flow, now we have access to sentiment analysis,

Keyword extraction and Language detection inside Microsoft Flow is now available.

In this post, I am going to show how to do sentiment analysis inside Power Automate.

First What is sentiment Analysis and what sort of analysis we have inside AI Builder for text analytics.

Text Analytics

in Power Automate and Power Apps three analytics is available as sentiment analytics, keyword extraction, and Language detection as below

As you can see in the below picture in AI builder under Build we have these three modules.

In this example, I am going to show sentiment analytics. The first login to account with AI builder account, then click on the Build and then click on the sentiment analysis

Then the new windows show the main purpose of the sentiment analysis. As you can see in the below picture, the only option to use is on Microsoft Power Automate (flow).

Next, you able to see some hints about using it in the Microsoft power Automate

Note: For long Text, analyze each sentence, not the whole document

Note: Use the same language for a document not multiple languages in a document

Then click on the Use in a Flow,

IN the Power Automate ( make sure you are in the same environment), then click on the solution and then click on the New then Flow.

In the new flow, click on the When a new email arrives

then choose the folder as inbox and the subject as Hotel

Next search for the predict for CDS, and for the model choose the SentimentAnalysis model, Then for the text choose the Body Preview,

Next for the language choose the language you will have as it is en for me

In the next step,  we are going to store the result in one drive for example, that you can provide a specific name for the file using the Concat function. In this example, I use the Utcnow to get the current time and date and the file type that is Text file as .txt.

for the file content, I am going to use the sentence to put in the file, before creating it, just click on the run you had from the previous example

as you can see we have a sentence-level sentiment analysis and a global level. I am going to Concat both of them.

then after the run code you should see below text file in the onedrive.

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If you don’t have a Power BI Premium capacity license, then you are limited to refresh your dataflows up to eight times a day, with the frequency of 30 minutes. The good news for you is that you have a way to do unlimited refreshes and with whatever frequency you like using Power Platform Dataflows. Read the rest of this blog article to learn how.

What is Dataflow?

In the world of Power BI, You can separate the data transformation layer using Dataflows. Dataflows can run one or multiple Power Query scripts on the cloud and store the result into Azure Data Lake. The result of the dataflow (which is stored in Azure Data Lake) can then be used in a Power BI dataset using Get Data from Power BI dataflows. If you want to learn more about Dataflows, I highly recommend reading my dataflows series that starts with this article.

Creating Power Platform Dataflows

Dataflow is not only a concept for Power BI, but It is also available as Power Platform dataflow. You can check out one of the announcements of features added in Power Platform Dataflows by Miguel Llopis (one of the product managers of the Data Integration team at Microsoft), here. You can create Power Platform Dataflows, very similar to the way that you create Power BI dataflows. The only difference at the beginning is where you create it. Power BI dataflows are created in Power BI service, Power Platform Dataflows can be created in Power Apps (or Power Platform) portal.

Login to Power Platform Portal here.

Then go to Dataflows under the Data section,

Start a New dataflow

just provide a name for the dataflow, and then create

At this step, you can either go ahead and get data from anywhere you want or use Blank Query and copy a Power Query script from Power BI Desktop into here.

after preparing your queries, you can go to the step of loading it.

Load to Entity

The load to entity step in Power Platform Dataflows is a bit different from Power BI Dataflows. In Power BI Dataflows, the result will be loaded into CSV files into Azure Data Lake, so no more configuration is needed. However, Power Platform Dataflows stores the data into CDS (Common Data Services), and you have to select the entity you want to load this data into it.

You can either choose one of the existing entities or create a new entity, then map fields. Because these are CDS entities, there are some rules for having key fields, name fields, etc. If you don’t know what the CDS is, and where it stores the data, read my article about CDS here.

Refresh Settings

After setting up the load to entity and mapping fields correctly, you will get into the Refresh settings. This is where you can refresh your dataflow even with the frequency of a minute. You can refresh it as many times as you want. You are not limited to eight times or even 48 times a day.

Refresh as much as you want

Here you can see the frequency of the test dataflow that I set up, which ran every minute.

What about Licensing?

I know what you are going to ask now! What about licensing? What type of license do you need for this? Well, for Power Platform dataflows, you don’t need a Power BI license at all, not Premium, not even Pro. You do need, however, to have a Power Apps license. at the time of writing this blog post, there are two options, $10 a month, and $40 a month. Both of these can be cheaper than Power BI Premium if you are a single user (or even a few users). The main difference between the two plans of Power Apps, as I see is the database and file size for CDS;

How to use the results in Power BI?

You can use the result of dataflows in Power BI (similar to the way that you can use the result of Power BI dataflows in Power BI). You need to Get Data from Power Platform Dataflows.

However, at the time of writing this blog post, this feature is still beta and under development, and might not show the Power Platform dataflows.

Another option is also to get data from the Common Data Service. (Because Power Platform Dataflows stores the data into CDS)

Note that Power BI Dataset Refresh Limites Still Applies

Having a Power Query script that can refresh even every minute is great. However, you have to note that the dataflow refresh is different from the dataset refresh. If you use the result of the dataflow in a Power BI dataset, Still you need to get your dataset refreshed, and that means you are limited to eight times a day or 48 times a day or API refreshes depends on your Power BI license limits.

However, having the ability to refresh even the dataflow more frequent, still can be useful for some scenarios. I bet if you came to this post by searching for such functionality, you have some ideas about that already .

Video

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As I mentioned in Post 1 and Post 2, AI builder is a game-changer in Power Apps and Flow, it provides the facility to easy embed AI in applications and process without writing any code. Also, there is no need to set up other environment and applications to make it work.

In this post, I am going to show you how to activate your Power Apps trial account to see and practice the AI Builder.

There is some note:

AI Builder in Power Apps available in Europe and the USA region.

There is a Trial (29 Days) and Production plan

for this scenario, I choose the USA and Trial plan to examine the AI Builder

Set Up Environment in Power Apps

To setup, the environment needs to login into Microsoft Power Apps Portal. Login

In the next step, you need to navigate to the Gear icon at the top, then click on the Admin Centre

this, navigate you to the admin centre environment.

As you can see in the environment page, all of my available account under RADACAD has been shown with their type, region and name. As the default region for RADACAD account in Australia, and AI Builder for now just available in USA and Europe, I need to create a new environment.  In this new page, you will see the Environments option.

In the new page, you need to provide the environment name, region ( in my scenario I choose the USA) and the environment Type.

It will take a couple of seconds to create the environment, and you will receive a message about it as well.

After creating the new environment to access to AI Builder, you need to create a database in Common Data Services(CDS)

another important note

AI Builder need to access to Common Data Service

Click on the Create database.

During the creating the database, it will ask you about the data filed for Currency and Language. in CDS, you able to mention, if you have a currency field what currency be the default one, or in what language you want to store your data.

after setting up all required parameters click on the Create database so your new environment access to CDS. Now you able to see the new environment with the name AI Builder 

Now you need to back to Power Apps main page, click on the Environment dropdown at the top, and select the AIBuilder option. As you can see in the below picture, on the left panel, the AI Builder (Preview) has been shown.

after setting up the AI Builder for Power Apps, you can see the same functionalities in Microsoft Flow.

you need to Login to Microsoft Flow,

at the top right click on the account name, and choose the environment that you already set up for  AI Builder. The same as Power Apps, you able to see the AI Builder on the left side.

to see how AI Builder Works see Post 2 and also more post on it coming soon.

Special thanks to Joe Fernandez from Power Apps team to help to examine these features.

1- https://docs.microsoft.com/en-us/power-platform/admin/create-environment 

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As mentioned in Part 1, AI Builder is a game changer in Power Apps and Microsoft flow.

In this post, I am going to show how you able to access the AI Builder for classification. 

to see how to set up your environment check the Part 1.

Get Data

to get data, you need to click on the Data–> Entities in the right panel

then, click on the Get Data option from the top menu.

in the menu, I am choosing the Text/CSV as I already load my dataset into Blob storage.

Next, just put the link to the blob storage and click the Next, if you access via a place that needs authentication then you need to set it up here, in this scenario, I do not access a private channel.

Now, you will access the data flow editor to clean and transform your data if needed ( remove columns, combine tables…), then put a name for the data and click on the Next.

In this part, you need to adjust and map your entity, if you already have the entity, in the Load setting, you need to choose the  first option ( load to the existing entity)

otherwise the second one.

then you should provide the Primary Name Field for the Titanic dataset, I choose the Name column as the passenger name. also, you may get an error regarding the change the data type to the text and you need to click on the delete rows that no longer in the query ( if you change something in the previous step), then simply click on the Next to load the data.

After loading the data, you need to refresh it, I choose to refresh it manually for the first time and click on the Creat.

Now you need to wait for data be loaded as an entity

NOTE! if you choose a binary classification, your column to predict should be in True and False format.

next, the data should be loaded and you should see a load status change to complete. otherwise, you need to back and check the data.

Now, you should see the data you loaded as a new entity in the Entity List.

AI Builder

Now we have data, need to create the model.

if you create the environment correctly, you should see the AI Builder in the left menu, click on the AI Builder and Build option.

Now the main page for the election of the AI model will be shown, For this scenario, we are going to choose the Binary Classification for prediction.

again be sure the column you are going to predict be in TRUE/FALSE format.

put a proper name for your model and click on the Create

Now the main process of creating the model will start.

The first step is to select which dataset need to select for training the model and what column you are going to predict.

Then, you need to choose which column of the dataset you need to be selected for training. In this example, I choose all of them which is not at all best practice but we will see how to back and change it.

Click on the next bottom and then, review what you have, if needed back and change the columns and if you happy click on the Train.

so you will see below message till the model trained

finally, you need to see the message like :

now I saw the below message, as you can see you able to check the overall accuracy, there is a training report and you able to create a New  Version of this with different data, moreover in the left pannel we are not anymore in the Build section but in the Models one.

Click on the View detail in the previous page, to see the influence report.

This report shows which column has more impact on the survival of people from the Titanic ship. Also, make sure to choose the one that has made sense from a business perspective,

Name of people does not impact, also Fair and PCLASS is the same thing.

I select the Age and Pclass only. If you click on the training report, my dataset has less than 1000 rows so the report does not show here as you can see in the below picture.

At this time, to create a new training model, you need to click on New Version

This returns you to the main training page.

Now you able to select the column you want from the list.

and click on the Next to train it again.

after all experiment, now you able to publish your model to be used in a Model-driven application.

In next post I will explain how to show the results

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