using System.Collections.Generic;
using Microsoft.ML;

public class SentimentAnalysis
{
    private readonly MLContext _mlContext;
    private PredictionEngine<SentimentData, SentimentPrediction> _predictionEngine;

    public SentimentAnalysis()
    {
        _mlContext = new MLContext();
        // Initialize the model here
    }

    public void UpdateModelWithNewData(IEnumerable<SentimentData> newData)
    {
        var trainData = _mlContext.Data.LoadFromEnumerable(newData);
        var pipeline = _mlContext.Transforms.Text.FeaturizeText("Features", nameof(SentimentData.Text))
            .Append(_mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: nameof(SentimentData.Label), featureColumnName: "Features"));
        var model = pipeline.Fit(trainData);
        _predictionEngine = _mlContext.Model.CreatePredictionEngine<SentimentData, SentimentPrediction>(model);
    }

    // Other methods for sentiment analysis
}

public class SentimentData
{
    public string Text { get; set; }
    public bool Label { get; set; }
}

public class SentimentPrediction
{
    public bool Prediction { get; set; }
    public float Probability { get; set; }
    public float Score { get; set; }
}