Sentiment analysis seeks to understand a subject’s attitude or emotional reaction towards a specific topic or brand. Sentiment analysis does not have to be complicated and technical. It could be something as simple as getting a person in your team to find what is being said about your brand and product on review page and identify how much of it is good and how much of isn’t. There is no need for a big budget and developer into complicated software, the cognitive service text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, language detection, and entity linking.
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.
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);
}
}
}
}
Step 4:
Posts
