Build a Sentiment Model using ML.Net with ML.NET Model Builder

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Build a Sentiment Model using
ML.Net
Eng Soon Cheah
Microsoft MVP
@cheahengsoon

2. Prerequisites
• Visual Studio 2017/ Visual Studio 2019
• ML.NET Model Builder
• Sentiment140 Dataset

3. Create the Project

4. As soon as the installation has completed open Visual Studio and create a new .NET Core console app.

5. Right-click on the project in Solution Explorer and select Add > Machine Learning. This opens the ML.NET Model Builder in a new
window in Visual Studio. Model Builder will help us through the process of building a machine learning model.

6. Building your model

7. To generate your model, you need to select a machine learning scenario. Model Builder offers several templates. Now we are
going to select the Sentiment Analysis scenario.

8. • In Model Builder, you can add data from a local file or connect to a SQL Server
database. In this case, you will add the dataset file you downloaded in the
Prerequisites step.
• Select File as the input data source in the drop-down, and in Select a file find and
select the dataset file. In my case is the mini dataset I created from the
Sentiment140 dataset.
• Under Column to predict (Label), select “0”. Since the Dataset has no headers it
takes the first row. You can add headers to avoid misunderstandings. The Label is
what you are predicting, which in this case is the Sentiment found in the first
column of the dataset. The rest of the columns are Features, which are attributes
that help predict the Label.
• If you are using the mini dataset 0 is for negative 1 is for positive. If you are using
the original dataset 0 is negative 2 is neutral and 4 is positive, so make a note of
this.

10. Train your Model

11. Select Start Training to start the training process.

12. You can keep track of the progress of model training in the Progress section.
• Status – This shows you the status of the model training process; this will tell
you how much time is left in the training process and will also tell you when
the training process has completed.
• Best accuracy – This shows you the accuracy of the best model that Model
Builder has found so far. Higher accuracy means the model predicted more
correctly on test data.
• Best algorithm – This shows you which algorithm performed the best so far
during Model Builder’s exploration.
• Last algorithm – This shows you the last algorithm that was explored by
Model Builder.

13. After model training finishes, go to the Evaluate step. The Evaluate step shows
you various outputs, including how many models were explored and the ML
task (in this case binary classification). Model Builder also displays the top 5
models explored and displays several evaluation metrics for each of those top 5
models, including AUC, AUPRC, and F1-score. You can find more informatio on
what those are here.

15. Run the project

16. • After evaluating your model, move on to the Code step. In the Code step in
Model Builder, select Add Projects.
• Model Builder adds both the machine learning model and the projects for
training and consuming the model to your solution. In the Solution Explorer,
you should see the code files that were generated by Model Builder.

18. References
• ML.NET Model Builder
https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-
builder

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