ML.NET Sentiment Demo



    The code first creates a new ML context. From this context, it loads the training data and splits it into a training and test set. Next, it defines the model's training algorithm and trains the model on the training set. Finally, it uses the model to make predictions on the test set.

    using Microsoft.ML;
    using Microsoft.ML.Data;
    using Microsoft.ML.Trainers;
    namespace SentimentAnalysis
      class Program
        static void Main(string[] args)
          // Create a new ML context
          var mlContext = new MLContext();
          // Load the training data
          var data = mlContext.Data.LoadFromTextFile<SentimentData>(
            hasHeader: true,
            separatorChar: ',');
          // Split the data into training and test sets
          var split = mlContext.Data.TrainTestSplit(data, testFraction: 0.2);
          // Define the model's training algorithm
          var pipeline = mlContext.Transforms.Text.FeaturizeText(
              outputColumnName: "Features",
              inputColumnName: nameof(SentimentData.SentimentText))
              labelColumnName: "Label",
              featureColumnName: "Features"));
          // Train the model
          var model = pipeline.Fit(split.TrainSet);
          // Use the model to make predictions on the test set
          var predictions = model.Transform(split.TestSet);
          // Evaluate the model's performance
          var metrics = mlContext.BinaryClassification.Evaluate(
            data: predictions,
            labelColumnName: "Label",
            scoreColumnName: "Score");
          Console.WriteLine("Accuracy: {0:P2}", metrics.Accuracy);
          Console.WriteLine("AUC: {0:P2}", metrics.AreaUnderRocCurve);
      // Define the data schema for the input data
      class SentimentData
        public bool Sentiment;
        public string SentimentText;
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