{"id":206,"date":"2017-05-24T01:48:09","date_gmt":"2017-05-24T08:48:09","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/power-platform\/blog\/power-apps\/custom-connectors-and-text-analytics-in-powerapps-part-one\/"},"modified":"2025-06-11T08:13:47","modified_gmt":"2025-06-11T15:13:47","slug":"custom-connectors-and-text-analytics-in-powerapps-part-one","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/power-platform\/blog\/power-apps\/custom-connectors-and-text-analytics-in-powerapps-part-one\/","title":{"rendered":"Using the Cognitive Services Text Analytics API in PowerApps"},"content":{"rendered":"

This post shows you how to build a basic app that uses the <\/a>Microsoft Cognitive Services Text Analytics API<\/a>. We’ll show you how to set up the Text Analytics API, and connect to it with the Text Analytics connector<\/a>. Then we’ll show you how to create an app that calls the API.<\/p>\n

Introduction to Microsoft Cognitive Services<\/h2>\n

Microsoft Cognitive Services are a set of APIs, SDKs, and services available to make your applications more intelligent, engaging, and discoverable. These services enable you to easily add intelligent features \u2013 such as emotion and video detection; facial, speech and vision recognition; and speech and language understanding \u2013 into your applications.<\/p>\n

We\u2019ll focus on “language understanding” for the rest of this post, working with the Text Analytics API. This API enables you to detect sentiment, key phrases, topics, and language from your text. Let’s get started by trying out a demo of the API, then signing up for a preview version.<\/p>\n

Try out the Text Analytics API<\/h3>\n

The API has an online demo \u2013 you can see how it works, and look at the JSON that the service returns.<\/p>\n

1. Go to the Text Analytics API<\/a> page.<\/p>\n

2. In the See it in action<\/b> section, use the example text, or enter your own text. Then click Analyze<\/b>.<\/p>\n

\"clip_image002[1]\"<\/a><\/p>\n

3. The page shows formatted results on the Analyzed text<\/b> tab, and the JSON response on the JSON<\/b> tab.<\/p>\n

Sign up for the Text Analytics API<\/h3>\n

The API is available as a free preview, and it is associated with an Azure subscription. You manage the API through the Azure portal.<\/p>\n

1. If you don\u2019t already have an Azure subscription, sign up for a free subscription<\/a>.<\/p>\n

2. Sign in to your Azure account.<\/p>\n

3. Go to the Create Cognitive Services blade<\/a> in the Azure portal.<\/p>\n

4. Enter information for the Text Analytics API, like in the following image. Select the F0<\/b> (free) pricing tier.<\/p>\n

\"clip_image003[1]\"<\/a><\/p>\n

5. In the lower-left corner, click Create<\/b>.<\/p>\n

6. On the Dashboard<\/b>, click the API that you just created.<\/p>\n

\"clip_image004[1]\"<\/a><\/p>\n

7. Click Keys<\/b>.<\/p>\n

\"clip_image005[1]\"<\/a><\/p>\n

8. Copy one of the keys on the right of the screen. We will use this key later when we create a connection to the API.<\/p>\n

\"clip_image006[1]\"<\/a><\/p>\n

Build the app<\/h2>\n

Now that we have the Text Analytics API up and running, we can connect to it from PowerApps, and build an app that calls the API. This is a single screen app that provides functionality similar to the demo on the Text Analytics API page. Download the completed app<\/a>, or get started on building it.<\/p>\n

Create the app and add a connection<\/h3>\n

First, we’ll create a blank phone app and add a connection with the Text Analytics<\/b> connector. If you need more information about these tasks, see Create an app from scratch<\/a> and Manage your connections in PowerApps<\/a>.<\/p>\n

1. In PowerApps Studio, click File<\/b> > New<\/b>, then under Blank app<\/b>, click Phone layout<\/b>.<\/p>\n

\"clip_image007[1]\"<\/a><\/p>\n

2. In the middle pane, click connect to data<\/b>.<\/p>\n

3. In the right pane, click New connection<\/b> > Text Analytics<\/b>.<\/p>\n

\"clip_image008[1]\"<\/a><\/p>\n

4. Copy your key into Account Key<\/b>, then click Create<\/b>.<\/p>\n

\"clip_image009[1]\"<\/a><\/p>\n

Add controls<\/h5>\n

The next step in creating the app is to add all the controls. Normally when I build apps, I add formulas to the controls as I go, but in this case I’ll focus on the controls first, then add a few formulas in the next section. The following image shows the app with all the controls.<\/p>\n

\"clip_image010[1]\"<\/a><\/p>\n

Follow the steps below to create this screen. If a control name is specified, that name is used in a formula in the next section.<\/p>\n

1. Add a Rectangle<\/b> control (under Insert<\/b> > Icons<\/b>) as a background for the title, then add a Label<\/b> control for the title. Add another Label<\/b> control for the introductory text.<\/p>\n

2. Add a Text input<\/b> control, so we can enter text to analyze. Name the control tiTextToAnalyze<\/b>. The app should now look like the following image.<\/p>\n

\"clip_image011[1]\"<\/a><\/p>\n

3. Add three Check box<\/b> controls, so we can choose which API operations to perform. Name the controls chkLanguage<\/b>, chkPhrases<\/b>, and chkSentiment<\/b>.<\/p>\n

4. Add a button, so we can call the API after selecting which operations to perform. The app should now look like the following image.<\/p>\n

\"clip_image012[1]\"<\/a><\/p>\n

5. Add three Label<\/b> controls. The first two will hold results from the language and sentiment API calls; the third is just an introduction for the gallery at the bottom of the screen.<\/p>\n

6. Add a Blank vertical gallery<\/b> control, then add a Label<\/b> control to the gallery. The gallery will hold results from the key phrases API call. The app should now look like the following image.<\/p>\n

\"clip_image013[1]\"<\/a><\/p>\n

We’re keeping this app simple to focus on calling the Text Analytics API, but you could add things \u2013 like logic to show and hide controls based on the check boxes selected, error handling if the user doesn’t select any options, and so on.<\/p>\n

Add formulas<\/h3>\n

OK, we have a nice-looking app, but it doesn’t do anything yet. We’ll fix that now. But before we dive into the details, let’s understand the pattern that the app follows:<\/p>\n

1. The app makes specific API calls based on the checkboxes selected in the app. When we click Analyze text<\/b>, the app makes 1, 2, or 3 API calls.<\/p>\n

2. Data that the API returns is stored in three different collections<\/a><\/i>: languageCollect<\/b>, sentimentCollect<\/b>, and phrasesCollect<\/b>.<\/p>\n

3. The Text<\/b> property for two of the labels, and the Items<\/b> property for the gallery, are updated based on what’s in the three collections.<\/p>\n

With that background, let\u2019s add the formula for the OnSelect<\/b> property of our button. This is where all the magic happens.<\/p>\n

If(chkLanguage.Value=true,<\/code><\/p>\n

ClearCollect(languageCollect, TextAnalytics.DetectLanguage({numberOfLanguagesToDetect:1, text:tiTextToAnalyze.Text}).detectedLanguages.name)<\/code><\/p>\n

);<\/code><\/p>\n

If(chkPhrases.Value=true,<\/code><\/p>\n

ClearCollect(phrasesCollect, TextAnalytics.KeyPhrases({language:\"en\", text:tiTextToAnalyze.Text}).keyPhrases)<\/code><\/p>\n

);<\/code><\/p>\n

If(chkSentiment.Value=true,<\/code><\/p>\n

ClearCollect(sentimentCollect, TextAnalytics.DetectSentiment({language:\"en\", text:tiTextToAnalyze.Text}).score)<\/code><\/p>\n

)<\/code><\/p>\n

There’s a bit going on here, so let’s break it down:<\/p>\n