BinaryClassification_SentimentAnalysis

Sentiment Analysis for User Reviews (F#)

ML.NET version	API type	Status	App Type	Data type	Scenario	ML Task	Algorithms
v1.3.1	Dynamic API	README.md updated	Console app	.tsv files	Sentiment Analysis	Two-class classification	Linear Classification

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In this introductory sample, you'll see how to use ML.NET to predict a sentiment (positive or negative) for customer reviews. In the world of machine learning, this type of prediction is known as binary classification.

Problem

This problem is centered around predicting if a customer's review has positive or negative sentiment. We will use wikipedia-detox-datasets (one dataset for training and a second dataset for model's accuracy evaluation) that were processed by humans and each comment has been assigned a sentiment label:

• 0 - negative
• 1 - positive

Using those datasets we will build a model that will analyze a string and predict a sentiment value of 0 or 1.

ML task - Binary classification

The generalized problem of binary classification is to classify items into one of two classes (classifying items into more than two classes is called multiclass classification).

• predict if an insurance claim is valid or not.
• predict if a plane will be delayed or will arrive on time.
• predict if a face ID (photo) belongs to the owner of a device.

The common feature for all those examples is that the parameter we want to predict can take only one of two values. In other words, this value is represented by boolean type.

Solution

To solve this problem, first we will build an ML model. Then we will train the model on existing data, evaluate how good it is, and lastly we'll consume the model to predict a sentiment for new reviews.

1. Build model

Building a model includes:

• Define the data's schema maped to the datasets to read (wikipedia-detox-250-line-data.tsv and wikipedia-detox-250-line-test.tsv) with a DataReader

• Create an Estimator and transform the data to numeric vectors so it can be used effectively by an ML algorithm (with FeaturizeText)

• Choosing a trainer/learning algorithm (such as FastTree) to train the model with.

The initial code is similar to the following:

    // STEP 1: Common data loading configuration
    let dataView = mlContext.Data.LoadFromTextFile<SentimentIssue>(dataPath, hasHeader = true)
    
    let trainTestSplit = mlContext.Data.TrainTestSplit(dataView, testFraction=0.2)
    let trainingDataView = trainTestSplit.TrainSet
    let testDataView = trainTestSplit.TestSet

    // STEP 2: Common data process configuration with pipeline data transformations          
    let dataProcessPipeline = mlContext.Transforms.Text.FeaturizeText("Features", "Text")

    // (OPTIONAL) Peek data (such as 2 records) in training DataView after applying the ProcessPipeline's transformations into "Features" 
    Common.ConsoleHelper.peekDataViewInConsole<SentimentIssue> mlContext trainingDataView dataProcessPipeline 2 |> ignore
    //Peak the transformed features column
    //Common.ConsoleHelper.peekVectorColumnDataInConsole mlContext "Features" trainingDataView dataProcessPipeline 1 |> ignore

    // STEP 3: Set the training algorithm, then create and config the modelBuilder                            
    let trainer = mlContext.BinaryClassification.Trainers.FastTree(labelColumnName = "Label", featureColumnName = "Features")
    let trainingPipeline = dataProcessPipeline.Append(trainer)

2. Train model

Training the model is a process of running the chosen algorithm on a training data (with known sentiment values) to tune the parameters of the model. It is implemented in the Fit() method from the Estimator object.

To perform training you need to call the Fit() method while providing the training dataset (wikipedia-detox-250-line-data.tsv file) in a DataView object.

    let trainedModel = trainingPipeline.Fit(trainingDataView)

3. Evaluate model

We need this step to conclude how accurate our model operates on new data. To do so, the model from the previous step is run against another dataset that was not used in training (wikipedia-detox-250-line-test.tsv). This dataset also contains known sentiments.

Evaluate() compares the predicted values for the test dataset and produces various metrics, such as accuracy, you can explore.

    // STEP 5: Evaluate the model and show accuracy stats
    printfn "===== Evaluating Model's accuracy with Test data ====="
    let predictions = trainedModel.Transform testDataView
    let metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label", "Score")

    Common.ConsoleHelper.printBinaryClassificationMetrics (trainer.ToString()) metrics

If you are not satisfied with the quality of the model, you can try to improve it by providing larger training datasets and by choosing different training algorithms with different hyper-parameters for each algorithm.

Keep in mind that for this sample the quality is lower than it could be because the datasets were reduced in size so the training is quick. You should use bigger labeled sentiment datasets to significantly improve the quality of your models.

4. Consume model

After the model is trained, we can use the Predict() API to predict the sentiment for new reviews.

    // Create prediction engine related to the loaded trained model
    let predEngine= trainedModel.CreatePredictionEngine<SentimentIssue, SentimentPrediction>(mlContext)

    //Score
    let resultprediction = predEngine.Predict(sampleStatement)

Where in resultprediction.PredictionLabel will be either true or false depending if it is a positive or negative predicted sentiment.