Build a Sentiment Model using ML.Net
Download as PPTX, PDF0 likes272 views
This document provides steps to build a sentiment analysis model using ML.NET Model Builder: 1. Create a .NET Core console app project in Visual Studio and add Machine Learning using Model Builder. 2. In Model Builder, select the Sentiment Analysis scenario and add the sentiment140 dataset, selecting the sentiment column as the label. 3. Train the model and evaluate the results, showing metrics like AUC and F1 score for the top 5 models. 4. Add the generated machine learning and code projects to the solution to train and consume the model.
Build a Sentiment Model using ML.Net
- 1. www.techconnect.io 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
- 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.
- 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