Sentiment Analysis In An ASP.NET Web Application With ML.NET

Training

With everything we have already established so far, we can already perform the training process and finally generate the desired model. In this section, the only thing we can customize is the maximum time in which ML.NET can search for the ideal model for text classification.

At the end of the training process, we can visualize how many models ML.NET evaluated, and the accuracy of the best model found.

Evaluation

Up to this point, we already have the model established, anyway, from the Model Builder of ML.NET we can evaluate the model with a particular example.

Consume

Finally, the interpreter will give us options to consume the created model, either by creating other projects (by console or with a web API) or indicating the calls we must make from our own application.

In this case, we can already consume the model from our web application with DotVVM on ASP.NET with the newly generated package: MLModel.mbconfig.

Part 2: Consume the generated model from ASP.NET

To consume the model we must refer to the MLModel class, which was generated by ML.NET in the group MLModel.mbconfig,

In this sense, it is necessary to establish an instance of ModelInput with the comment we want to predict, and then call the Predict method, and obtain an object of type ModelOutput with the corresponding prediction (1 positive, 0 negative), and its probability.

ViewModel

For this purpose, and to be able to establish a web page with DotVVM, in a ViewModel, in this case, the DefaultViewModel.cs, we will define three attributes or global variables,

public string InputText { get; set; }
public string Result { get; set; } = null;
public decimal? Score { get; set; } = null;

These variables will allow us to store the text entered by the user in InputText, and represent the results of the prediction in the attributes Result and Score.

Then we can set a method in this class, in such a way that it represents the action of a button to make the prediction,

public void Predict() {
    var sampleData = new MLModel.ModelInput() {
        Col0 = InputText,
    };
    //Load model and predict output
    var output = MLModel.Predict(sampleData);
    if ((int) output.Prediction == 0) {
        Result = "Negative.";
    } else {
        Result = "Positive.";
    }
    Score = decimal.Round((decimal)(output.Score[(int) output.Prediction] * 100), 2);
}

In this code, we have three important parts,

• Initialize an MLModel.ModelInput with the comment entered by the user.
• Call the MLModel.Predict with the ModelInput as a parameter, thus obtaining the ModelOutput with the results.
• Prepare the Result to visualize the result later (positive or negative), and its accuracy in the prediction.

View

Very well, we can now construct the view by referencing the attributes and methods created in the ViewModel. This view will be encoded in the file Default.dothtml as follows,

<table style="border: hidden">
    <tbody style="border: hidden">
        <tr style="border: hidden">
            <td style="border: hidden" align="center">
                <dot:TextBox Text="{value: InputText}" placeholder="Write your text..." />
            </td>
            <td style="border: hidden" align="center">
                <dot:Button Click="{command: Predict()}" Text="Predict" />
            </td>
            <td style="border: hidden" align="center">
                <b>{{value: Result}}</b> {{value: Score}}
            </td>
        </tr>
    </tbody>
</table>

Here the most important thing is the TextBox control for the user to enter their comment and store it in the InputText attribute, the call to the Predict() method with a Button, and the display of the results by displaying the values of the variables Result, and Score.

Examples running

With this view and its ViewModel, our website is ready. Here we can see two examples at runtime,

The source code for this demo is available in this GitHub repository: github.com/esdanielgomez/MLNET_SentimentAnalysis.

Thank you!

Thank you very much for reading, I hope this demo can be useful to you. If you have any questions or ideas that you need to discuss, it will be a pleasure to be able to collaborate and together exchange knowledge with each other.