BinaryClassification_Sentiment_Razor

page_type	name	description	urlFragment	languages	products
sample	Train a sentiment analysis machine learning model with ML.NET Model Builder	Train a machine learning model to analyze and classify sentiment using ML.NET Model Builder	mlnet-sentiment-analysis-model-builder	csharp	dotnet dotnet-core aspnet-core vs vs-ide mlnet

Sentiment Analysis: Razor Pages sample optimized for scalability and performance when running/scoring an ML.NET model built with Model Builder (Using the new '.NET Core Integration Package for ML.NET')

ML.NET version	Status	App Type	Data type	Scenario	ML Task	Algorithms
v1.3.1	Up-to-date	Razor Pages	Single data sample	Sentiment Analysis	Binary classification	Linear Classification

Goal

Create a Razor Pages web application that hosts an ML.NET binary classification machine learning model trained using Model Builder to analyze the sentiment of comments from a website.

Application

• SentimentRazor: A .NET Core Razor Pages web application that uses a binary classification model to analyze sentiment from comments made on the website.
• SentimentRazorML.ConsoleApp: A .NET Core Console application that contains the model training and test prediction code.
• SentimentRazorML.Model: A .NET Standard class library containing the data models that define the schema of input and output model data as well as the persisted version of the best performing model during training.

The data

Each row in the wikipedia-detox-250-line-data.tsv dataset represents a different review left by a user on Wikipedia. The first column represents the sentiment of the text (0 is non-toxic, 1 is toxic), and the second column represents the comment left by the user. The columns are separated by tabs. The data looks like the following:

Sentiment	SentimentText
1	==RUDE== Dude, you are rude upload that carl picture back, or else.
1	== OK! == IM GOING TO VANDALIZE WILD ONES WIKI THEN!!!
0	I hope this helps.

The model

The goal of the application is to predict whether a comment's sentiment belongs to one of two categories (toxic/not-toxic). The Machine Learning Task to use in this scenario is binary classification. The model in this application was trained using Model Builder.

Model Builder is an intuitive graphical Visual Studio extension to build, train, and deploy custom machine learning models.

Model Builder uses automated machine learning (AutoML) to explore different machine learning algorithms and settings to help you find the one that best suits your scenario.

You don't need machine learning expertise to use Model Builder. All you need is some data, and a problem to solve. Model Builder generates the code to add the model to your .NET application.

In this solution, both the SentimentRazorML.ConsoleApp and SentimentRazorML.Model projects are autogenerated by Model Builder.

The web application

Users interact with the application through a Razor Pages website. In a text box on the main page of the application, a user enters a comment which triggers a handler on the page's model to use the input to predict the sentiment of the comment using the trained model.

Challenges

A challenge when making a single prediction with an ML.NET model in multi-threaded applications is that the PredictionEngine is not thread-safe.

Solutions

For improved performance and thread safety, use the PredictionEnginePool service, which creates an ObjectPool of PredictionEngine objects for application use. To use it within your application, add the Microsoft.Extensions.ML NuGet package to your project and register the PredictionEnginPool as you would any other dependency inside the Startup class of the SentimentRazorML project.

services.AddPredictionEnginePool<ModelInput, ModelOutput>()
        .FromFile(_modelPath);

Then, inside the handler or action where the prediction is to be made, call use Predict method like you would with a PredictionEngine.

var prediction = _predictionEnginePool.Predict(input);

Try a different dataset

If you want to try out the application with a dataset that produces better results such as the UCI Sentiment Labeled Sentences dataset, you can make the following adjustments.

Get the data

1. Download UCI Sentiment Labeled Sentences dataset ZIP file anywhere on your computer, and unzip it.

2. Open PowerShell and navigate to the unzipped folder in the previous step

3. By default, the file does not have column names. To add column names to the training data, use the following PowerShell commands:

   echo "Comment`tSentiment" | sc yelp_labelled_columns.tsv; cat yelp_labelled.tsv | sc yelp_labelled_columns.tsv	

The output generated by the previous commands is a new file called yelp_labelled_columns.tsv containing the original data with the respective column names.

Each row in the yelp_labelled_columns.tsv dataset represents a different restaurant review left by a user on Yelp. The first column represents the comment left by the user, and the second column represents the sentiment of the text (0 is negative, 1 positive). The columns are separated by tabs. The data looks like the following:

Comment	Sentiment
Wow... Loved this place.	1
Crust is not good.	0
Not tasty and the texture was just nasty.	0

Train and use the model

1. Use model builder to train a binary classification model using the new dataset.
2. Update the OnGetAnalyzeSentiment handler in the Index.cshtml.cs file.

public IActionResult OnGetAnalyzeSentiment([FromQuery] string text)
{
        if (String.IsNullOrEmpty(text)) return Content("Neutral");
        var input = new ModelInput { Comment = text };
        var prediction = _predictionEnginePool.Predict(input);
        var sentiment = Convert.ToBoolean(prediction.Prediction) ? "Positive" : "Negative";
        return Content(sentiment);
}

3. Update the updateSentiment function in the site.js file

function updateSentiment() {

var userInput = $("#Message").val();

getSentiment(userInput)
        .then((sentiment) => {
        switch (sentiment) {
                case "Positive":
                updateMarker(100.0,sentiment);
                break;
                case "Negative":
                updateMarker(0.0,sentiment);
                break;
                default:
                updateMarker(45.0, "Neutral");
        }
        });
}