Navigate to LUIS Portal in a browser and sign in. if you are trying to create first time signing in, you need to create free LUIS account.

Step 2: 

Click on “Create New App” from portal menu options.

Step 3: 

Provide name, culture and description in the following information into the new app screen.

Name - provide the relevant to your application and Unique LUIS app name

Culture – Luis will support multiple language, now we will select as English Language

Description - Provide the Description of app and its optional field



Once completed, Click on Done button and Select “Build” menu from main menu.

Create Intents:

Intent is an action the user wants to perform. Natural language is extremely flexible. in fact, different languages and cultures make them less or more flexible. But what is the user’s "intention?" It’s the goal the user is trying to achieve. Maybe they want to turn the lights on. Maybe they want to schedule a calendar appointment. Maybe they want to Send Email. Whatever the intent may be, the user can express themselves in a multitude of ways, but it always translates to one intent. LUIS understands utterances and converts them to one or more intents that you specify in our LUIS application. There are two type intent

New Intent – you can create custom intent-based user needs

Add prebuild domain Intent - you can use prebuilt intents from one of the prebuilt domains.

Select Intents > Click on New intent or Add prebuilt domain intent 

Prebuilt domain intents are parts of prebuilt domains, you can select inbuild category and intents from the list, here, we will select sending email and send email prebuilt domain intents 

Create Entities:

When the user issues a command as an utterance, that utterance is converted into an intent. But hidden inside that utterance is important data that’s needed to complete the action. For example, "turn the lights off" represents an action called "off;" what entity is it working on? It’s working on lights. You need to extract a single entity out of this utterance.

Select Entities > Add the Prebuild domain entity 

LUIS comes with a number of prebuilt domain models with prebuilt entities. Or you can quite easily create a new custom domain model with custom entities. Here we are creating Prebuilt domain entry 

In order to train the model, you’ll see a Train button on the top right-hand corner


Now you can start publish our LUIS application



While publish application, will be available multiple environment, let we start “Production” environment and publish


after publish its available in publicly the end point URL




Here you can view your publish settings, as well as your LUIS App ID and Password. You will need these to access your LUIS app from your client applications. Please note that you can also type in queries directly to your production endpoint, and you’ll get a JSON response from LUIS

When your model is trained and up-to-date, you can then open the test panel and provide some utterances to your LUIS model



You can copy paste the URL highlighted in key Endpoint setting screen and under the endpoint column (note that the app ID and keys are removed for this image) into an internet browser, and type the query – “Please email to Suthahar”. Below is the full JSON response for the query

Summary

In this article, you learned about how to create Natural Language Understanding app with LUIS .If you have any questions/ feedback/ issues, please write them in the comment box.

The most of the companies and brands now use sentiment analysis to find out what people are saying about them on social media. A bad review on social media can destroy a brand’s reputation if ignored or poorly handled. They aren’t simply rating their experience with 1 star or 5 stars. They’re also expressing their thoughts, feeling, expectations in free form text. This can be challenging to handle, especially if your company is getting a lot of feedback. When you have tens or even hundreds of thousands of feedbacks to read and manage, its easy to use cognitive text analytics service API.

The Sentiment Analysis API evaluates text input and returns a sentiment score for each document, ranging from 0 (negative) to 1 (positive). This capability is useful for detecting positive and negative sentiment in social media, customer reviews, and discussion forums. Content is provided by you, models and training data are provided by the service.

Currently, Sentiment Analysis supports English, German, Spanish, and French. Other languages are in preview. In this article, I will show how we can integrate sentiment API from Xamarin Mobile application using visual studio 2019

Text Analytics API Price:

The Text Analytics API can be purchased in units of the S0-S4 tier at a fixed price. Each unit of a tier comes with included quantities of API transactions. If the user exceeds the included quantities, overages are charged at the rate specified in the pricing table below. These overages are prorated and the service is billed on a monthly basis. The included quantities in a tier are reset each month. In the S tier, the service is billed for only the amount of Text Records submitted to the service. You can read more about pricing based on country check out here.

Create Text Analytics API Key:

You need to create an Azure account, generate API key and end point URL based region for implementation to the Xamarin Mobile application.

Step 1: 

Create Free Azure subscription and Login to portal

Step 2: 

Create On “+ Create a resource “> Under Azure Marketplace, select AI + Machine learning and discover the list of available featured. > Select “Text Analytics”

Step 3: 

 On the create page, provide the name, pricing, resource group and click on Create.

Step 4:

Wait for a few seconds. After the Cognitive Services account is successfully deployed, click the notification or tile in the dashboard to view the account information. You can copy the Endpoint URL and Key in the Overview section for API calls in our Xamarin applications.

Build Xamarin Forms Application using Visual Studio 2019:

Let's start with creating a new Xamarin Forms Project using Visual Studio 2019. When accessing Visual Studio 2019 for the first time, you will come across a new interface for opening a creating the projects.

Open Run >> Type “Devenev.Exe” and enter >> Create New Project (Ctrl+Shift+N) or select open recent application.



The available templates will appear on a window like below. Select Xamarin Forms application with different mobile platform.



Provide project name, Location and solution name in the following configure new project screen



Select as Blank apps and select the platform



The Solution will be created with all the platform and PCL projects.

PCL UI Design:

The UI will have a few elements on the screen and overlay content view window. Editor control for providing user input value and overlay window for show the result.

You can add Newtonsoft.JSON to solutions. Right click on Solutions > Manage NuGet Packages > select Newtonsoft.Json from Browse tab > click on Install.

<?xml version="1.0" encoding="utf-8" ?>
<ContentPage xmlns="http://xamarin.com/schemas/2014/forms"
xmlns:x="http://schemas.microsoft.com/winfx/2009/xaml"
xmlns:local="clr-namespace:MobileFeedback"
:Class="MobileFeedback.MainPage">
<ContentPage.Content>
<AbsoluteLayout>
<!-- Normal Page Content -->
<StackLayout AbsoluteLayout.LayoutBounds="0, 0, 1, 1"
AbsoluteLayout.LayoutFlags="All">
<Image Source="product.gif" VerticalOptions="Start" HorizontalOptions="Start" Margin="0,0,0,0" ></Image>
<Editor x:Name="txtfeedback" WidthRequest="100" HeightRequest="200"></Editor>
<Button Text="Submit" Clicked="Submit_Clicked"></Button>
</StackLayout>
<!-- Overlay -->
<ContentView x:Name="overlay"
AbsoluteLayout.LayoutBounds="0, 0, 1, 1"
AbsoluteLayout.LayoutFlags="All"
IsVisible="False"
BackgroundColor="#C0808080"
Padding="10, 0">
<StackLayout Orientation="Vertical"
BackgroundColor="White"
HeightRequest="175"
WidthRequest="300"
HorizontalOptions="Center"
VerticalOptions="Start"
Margin="0,20,0,0" >
<Image x:Name="imgstatus" WidthRequest="70" HeightRequest="70"></Image>
<Label Text="" x:Name="lblStatus"></Label>
<StackLayout Orientation="Horizontal" HorizontalOptions="Center">
<Button Text="OK" FontSize="Small"
VerticalOptions="CenterAndExpand"
HorizontalOptions="Center"
Clicked="OnOKButtonClicked" />
</StackLayout>
</StackLayout>
</ContentView>
</AbsoluteLayout>
</ContentPage.Content>
</ContentPage>

Create Document Entity Class:

Create class for Document class, it will deserialize the response and return an object of type TextAnalyticsResponse.The response format defined by the API looks like below document entity
using System;
using System.Collections.Generic;
using System.Text;
namespace MobileFeedback
{
class Document
{
public string Id { get; set; }
public double? Score { get; set; }
}
class TextAnalyticsResponse
{
public List<Document> Documents { get; set; }
}
}

Create SentimentAnalysisHelper Helper Class:

You can replace Text Analytics API service endpoint and subscription key. If you don't already have these go back to the previous steps. Below is the complete class you need to add. we have to appended /sentiment to the end of the ApiUri in order to invoke the sentiment operation.



using Newtonsoft.Json;
using System;
using System.Collections.Generic;
using System.Net.Http;
using System.Text;
namespace MobileFeedback
{
static class SentimentAnalysisHelper
{
private const string ApiUri = "<API url>”;
private const string SubscriptionKey = "<your Key>";
private const string Text = "The food was delicious and there were wonderful staff.";
private static readonly HttpClient Client = GetClient();
private static HttpClient GetClient()
{
var client = new HttpClient();
client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", SubscriptionKey);
client.DefaultRequestHeaders.Add("ContentType", "application/json");
client.DefaultRequestHeaders.Add("Accept", "application/json");
return client;
}
private static TextAnalyticsResponse DeserializeTextAnalyticsResponse(string json)
{
return JsonConvert.DeserializeObject<TextAnalyticsResponse>(json);
}
public static TextAnalyticsResponse GetSentiment(string text)
{
var body = JsonConvert.SerializeObject(new
{
Documents = new object[]
{
new
{
Text = text,
Id = Guid.NewGuid()
}
}
});
using (var content = new ByteArrayContent(Encoding.UTF8.GetBytes(body)))
{
var responseMessage = Client.PostAsync(ApiUri, content).Result;
responseMessage.EnsureSuccessStatusCode();
var json = responseMessage.Content.ReadAsStringAsync().Result;
return DeserializeTextAnalyticsResponse(json);
}
}
}
}

You can add following to code behind in the design file. we'll add a method(GetSentiment) to call the Text Analytics API sentiment endpoint. It will deserialize the response and return an object of type TextAnalyticsResponse .The method will take a string text as input, create the request body, and then send it to the Text Analytics API using an HttpClient instance.
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Xamarin.Forms;
using System.Net.Http;
using System.Threading;
namespace MobileFeedback
{
public partial class MainPage : ContentPage
{
public MainPage()
{
InitializeComponent();
}
private void Submit_Clicked(object sender, EventArgs e)
{
TextAnalyticsResponse result = SentimentAnalysisHelper.GetSentiment(txtfeedback.Text);
double? score = result.Documents.FirstOrDefault().Score;
if(score < 0.5)
{
imgstatus.Source = "bad.png";
lblStatus.Text = "This is the first time we have heard of this problem. Thank you for pointing it out to us. I assure you we will do our best to prevent it from happening again, We will contact with you regardng the issue";
}
else if(score > 0.5 && score < 0.9)
{
imgstatus.Source = "okay.png";
lblStatus.Text = "We are always eager to get feedback from our customers. Thank you for taking the time to write to us.We will improve our service and our team will contact you";
}
else if(score> 0.9)
{
imgstatus.Source = "happy.png";
lblStatus.Text = "Your valuable feedback will assist us in our continuing effort to provide our users with the best possible support experience.";
}
overlay.IsVisible = true;
}
void OnOKButtonClicked(object sender, EventArgs args)
{
overlay.IsVisible = false;
}
void OnCancelButtonClicked(object sender, EventArgs args)
{
overlay.IsVisible = false;
}
}
}

We have completed the code for consuming TextAnalytics API. Now, we can select the platform and press F5. The output looks like below

Summary

In this article, you learned how to consuming TextAnalytics API and automate customer feedback without using rating. I hope this article will help you. Please leave your feedback/query using the comments box, if you like this article, please share it with your friends.