diff --git a/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs b/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs
index f99fefe378..701864c577 100644
--- a/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs
@@ -23,315 +23,35 @@ public partial class ScenariosTests
         [Fact]
         public void TrainAndPredictSentimentModelTest()
         {
-            string dataPath = GetDataPath(SentimentDataPath);
-            var pipeline = new LearningPipeline();
-
-            pipeline.Add(new Data.TextLoader(dataPath)
-            {
-                Arguments = new TextLoaderArguments
-                {
-                    Separator = new[] { '\t' },
-                    HasHeader = true,
-                    Column = new[]
-                    {
-                        new TextLoaderColumn()
-                        {
-                            Name = "Label",
-                            Source = new [] { new TextLoaderRange(0) },
-                            Type = Runtime.Data.DataKind.Num
-                        },
-
-                        new TextLoaderColumn()
-                        {
-                            Name = "SentimentText",
-                            Source = new [] { new TextLoaderRange(1) },
-                            Type = Runtime.Data.DataKind.Text
-                        }
-                    }
-                }
-            });
-
-            pipeline.Add(new TextFeaturizer("Features", "SentimentText")
-            {
-                KeepDiacritics = false,
-                KeepPunctuations = false,
-                TextCase = TextNormalizerTransformCaseNormalizationMode.Lower,
-                OutputTokens = true,
-                StopWordsRemover = new PredefinedStopWordsRemover(),
-                VectorNormalizer = TextTransformTextNormKind.L2,
-                CharFeatureExtractor = new NGramNgramExtractor() { NgramLength = 3, AllLengths = false },
-                WordFeatureExtractor = new NGramNgramExtractor() { NgramLength = 2, AllLengths = true }
-            });
-
-            pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2 });
-            pipeline.Add(new PredictedLabelColumnOriginalValueConverter() { PredictedLabelColumn = "PredictedLabel" });
-
-            PredictionModel<SentimentData, SentimentPrediction> model = pipeline.Train<SentimentData, SentimentPrediction>();
-            IEnumerable<SentimentData> sentiments = new[]
-            {
-                new SentimentData
-                {
-                    SentimentText = "Please refrain from adding nonsense to Wikipedia."
-                },
-                new SentimentData
-                {
-                    SentimentText = "He is a CHEATER, and the article should say that."
-                }
-            };
-
-            IEnumerable<SentimentPrediction> predictions = model.Predict(sentiments);
-
-            Assert.Equal(2, predictions.Count());
-            Assert.True(predictions.ElementAt(0).Sentiment.IsFalse);
-            Assert.True(predictions.ElementAt(1).Sentiment.IsTrue);
-
-            string testDataPath = GetDataPath(SentimentTestPath);
-            var testData = new Data.TextLoader(testDataPath)
-            {
-                Arguments = new TextLoaderArguments
-                {
-                    Separator = new[] { '\t' },
-                    HasHeader = true,
-                    Column = new[]
-                    {
-                        new TextLoaderColumn()
-                        {
-                            Name = "Label",
-                            Source = new [] { new TextLoaderRange(0) },
-                            Type = Runtime.Data.DataKind.Num
-                        },
-
-                        new TextLoaderColumn()
-                        {
-                            Name = "SentimentText",
-                            Source = new [] { new TextLoaderRange(1) },
-                            Type = Runtime.Data.DataKind.Text
-                        }
-                    }
-                }
-            };
-
+            var pipeline = PreparePipeline();
+            var model = pipeline.Train<SentimentData, SentimentPrediction>();
+            var testData = PrepareTextLoaderTestData();
             var evaluator = new BinaryClassificationEvaluator();
             BinaryClassificationMetrics metrics = evaluator.Evaluate(model, testData);
-
-            Assert.Equal(.5556, metrics.Accuracy, 4);
-            Assert.Equal(.8, metrics.Auc, 1);
-            Assert.Equal(.87, metrics.Auprc, 2);
-            Assert.Equal(1, metrics.Entropy, 3);
-            Assert.Equal(.6923, metrics.F1Score, 4);
-            Assert.Equal(.969, metrics.LogLoss, 3);
-            Assert.Equal(3.083, metrics.LogLossReduction, 3);
-            Assert.Equal(1, metrics.NegativePrecision, 3);
-            Assert.Equal(.111, metrics.NegativeRecall, 3);
-            Assert.Equal(.529, metrics.PositivePrecision, 3);
-            Assert.Equal(1, metrics.PositiveRecall);
-
-            ConfusionMatrix matrix = metrics.ConfusionMatrix;
-            Assert.Equal(2, matrix.Order);
-            Assert.Equal(2, matrix.ClassNames.Count);
-            Assert.Equal("positive", matrix.ClassNames[0]);
-            Assert.Equal("negative", matrix.ClassNames[1]);
-
-            Assert.Equal(9, matrix[0, 0]);
-            Assert.Equal(9, matrix["positive", "positive"]);
-            Assert.Equal(0, matrix[0, 1]);
-            Assert.Equal(0, matrix["positive", "negative"]);
-
-            Assert.Equal(8, matrix[1, 0]);
-            Assert.Equal(8, matrix["negative", "positive"]);
-            Assert.Equal(1, matrix[1, 1]);
-            Assert.Equal(1, matrix["negative", "negative"]);
+            ValidateExamples(model);
+            ValidateBinaryMetrics(metrics);
         }
 
         [Fact]
         public void TrainTestPredictSentimentModelTest()
         {
-            string dataPath = GetDataPath(SentimentDataPath);
-            var pipeline = new LearningPipeline();
-
-            pipeline.Add(new Data.TextLoader(dataPath)
-            {
-                Arguments = new TextLoaderArguments
-                {
-                    Separator = new[] { '\t' },
-                    HasHeader = true,
-                    Column = new[]
-                    {
-                        new TextLoaderColumn()
-                        {
-                            Name = "Label",
-                            Source = new [] { new TextLoaderRange(0) },
-                            Type = Runtime.Data.DataKind.Num
-                        },
-
-                        new TextLoaderColumn()
-                        {
-                            Name = "SentimentText",
-                            Source = new [] { new TextLoaderRange(1) },
-                            Type = Runtime.Data.DataKind.Text
-                        }
-                    }
-                }
-            });
-
-            pipeline.Add(new TextFeaturizer("Features", "SentimentText")
-            {
-                KeepDiacritics = false,
-                KeepPunctuations = false,
-                TextCase = TextNormalizerTransformCaseNormalizationMode.Lower,
-                OutputTokens = true,
-                StopWordsRemover = new PredefinedStopWordsRemover(),
-                VectorNormalizer = TextTransformTextNormKind.L2,
-                CharFeatureExtractor = new NGramNgramExtractor() { NgramLength = 3, AllLengths = false },
-                WordFeatureExtractor = new NGramNgramExtractor() { NgramLength = 2, AllLengths = true }
-            });
-
-            pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2 });
-            pipeline.Add(new PredictedLabelColumnOriginalValueConverter() { PredictedLabelColumn = "PredictedLabel" });
-
+            var pipeline = PreparePipeline();
             PredictionModel<SentimentData, SentimentPrediction> model = pipeline.Train<SentimentData, SentimentPrediction>();
-            IEnumerable<SentimentData> sentiments = new[]
-            {
-                new SentimentData
-                {
-                    SentimentText = "Please refrain from adding nonsense to Wikipedia."
-                },
-                new SentimentData
-                {
-                    SentimentText = "He is a CHEATER, and the article should say that."
-                }
-            };
-
-            string testDataPath = GetDataPath(SentimentTestPath);
-            var testData = new Data.TextLoader(testDataPath)
-            {
-                Arguments = new TextLoaderArguments
-                {
-                    Separator = new[] { '\t' },
-                    HasHeader = true,
-                    Column = new[]
-                    {
-                        new TextLoaderColumn()
-                        {
-                            Name = "Label",
-                            Source = new [] { new TextLoaderRange(0) },
-                            Type = Runtime.Data.DataKind.Num
-                        },
-
-                        new TextLoaderColumn()
-                        {
-                            Name = "SentimentText",
-                            Source = new [] { new TextLoaderRange(1) },
-                            Type = Runtime.Data.DataKind.Text
-                        }
-                    }
-                }
-            };
-
+            var testData = PrepareTextLoaderTestData();
             var tt = new TrainTestEvaluator().TrainTestEvaluate<SentimentData, SentimentPrediction>(pipeline, testData);
 
             Assert.Null(tt.ClassificationMetrics);
             Assert.Null(tt.RegressionMetrics);
             Assert.NotNull(tt.BinaryClassificationMetrics);
             Assert.NotNull(tt.PredictorModels);
-
-            BinaryClassificationMetrics metrics = tt.BinaryClassificationMetrics;
-            Assert.Equal(.5556, metrics.Accuracy, 4);
-            Assert.Equal(.8, metrics.Auc, 1);
-            Assert.Equal(.87, metrics.Auprc, 2);
-            Assert.Equal(1, metrics.Entropy, 3);
-            Assert.Equal(.6923, metrics.F1Score, 4);
-            Assert.Equal(.969, metrics.LogLoss, 3);
-            Assert.Equal(3.083, metrics.LogLossReduction, 3);
-            Assert.Equal(1, metrics.NegativePrecision, 3);
-            Assert.Equal(.111, metrics.NegativeRecall, 3);
-            Assert.Equal(.529, metrics.PositivePrecision, 3);
-            Assert.Equal(1, metrics.PositiveRecall);
-
-            ConfusionMatrix matrix = metrics.ConfusionMatrix;
-            Assert.Equal(2, matrix.Order);
-            Assert.Equal(2, matrix.ClassNames.Count);
-            Assert.Equal("positive", matrix.ClassNames[0]);
-            Assert.Equal("negative", matrix.ClassNames[1]);
-
-            Assert.Equal(9, matrix[0, 0]);
-            Assert.Equal(9, matrix["positive", "positive"]);
-            Assert.Equal(0, matrix[0, 1]);
-            Assert.Equal(0, matrix["positive", "negative"]);
-
-            Assert.Equal(8, matrix[1, 0]);
-            Assert.Equal(8, matrix["negative", "positive"]);
-            Assert.Equal(1, matrix[1, 1]);
-            Assert.Equal(1, matrix["negative", "negative"]);
-
-            IEnumerable<SentimentPrediction> predictions = tt.PredictorModels.Predict(sentiments);
-            Assert.Equal(2, predictions.Count());
-            Assert.True(predictions.ElementAt(0).Sentiment.IsFalse);
-            Assert.True(predictions.ElementAt(1).Sentiment.IsTrue);
-
-            predictions = tt.PredictorModels.Predict(sentiments);
-            Assert.Equal(2, predictions.Count());
-            Assert.True(predictions.ElementAt(0).Sentiment.IsFalse);
-            Assert.True(predictions.ElementAt(1).Sentiment.IsTrue);
+            ValidateExamples(tt.PredictorModels);
+            ValidateBinaryMetrics(tt.BinaryClassificationMetrics);
         }
 
         [Fact]
         public void CrossValidateSentimentModelTest()
         {
-            string dataPath = GetDataPath(SentimentDataPath);
-            var pipeline = new LearningPipeline();
-
-            pipeline.Add(new Data.TextLoader(dataPath)
-            {
-                Arguments = new TextLoaderArguments
-                {
-                    Separator = new[] { '\t' },
-                    HasHeader = true,
-                    Column = new[]
-                    {
-                        new TextLoaderColumn()
-                        {
-                            Name = "Label",
-                            Source = new [] { new TextLoaderRange(0) },
-                            Type = Runtime.Data.DataKind.Num
-                        },
-
-                        new TextLoaderColumn()
-                        {
-                            Name = "SentimentText",
-                            Source = new [] { new TextLoaderRange(1) },
-                            Type = Runtime.Data.DataKind.Text
-                        }
-                    }
-                }
-            });
-
-            pipeline.Add(new TextFeaturizer("Features", "SentimentText")
-            {
-                KeepDiacritics = false,
-                KeepPunctuations = false,
-                TextCase = TextNormalizerTransformCaseNormalizationMode.Lower,
-                OutputTokens = true,
-                StopWordsRemover = new PredefinedStopWordsRemover(),
-                VectorNormalizer = TextTransformTextNormKind.L2,
-                CharFeatureExtractor = new NGramNgramExtractor() { NgramLength = 3, AllLengths = false },
-                WordFeatureExtractor = new NGramNgramExtractor() { NgramLength = 2, AllLengths = true }
-            });
-
-            pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2 });
-            pipeline.Add(new PredictedLabelColumnOriginalValueConverter() { PredictedLabelColumn = "PredictedLabel" });
-
-            IEnumerable<SentimentData> sentiments = new[]
-            {
-                new SentimentData
-                {
-                    SentimentText = "Please refrain from adding nonsense to Wikipedia."
-                },
-                new SentimentData
-                {
-                    SentimentText = "He is a CHEATER, and the article should say that."
-                }
-            };
+            var pipeline = PreparePipeline();
 
             var cv = new CrossValidator().CrossValidate<SentimentData, SentimentPrediction>(pipeline);
 
@@ -343,7 +63,7 @@ public void CrossValidateSentimentModelTest()
             Assert.Equal(4, cv.BinaryClassificationMetrics.Count());
 
             //Avergae of all folds.
-            BinaryClassificationMetrics metrics = cv.BinaryClassificationMetrics[0];
+            var metrics = cv.BinaryClassificationMetrics[0];
             Assert.Equal(0.57023626091422708, metrics.Accuracy, 4);
             Assert.Equal(0.54960689910161487, metrics.Auc, 1);
             Assert.Equal(0.67048277219704255, metrics.Auprc, 2);
@@ -386,7 +106,7 @@ public void CrossValidateSentimentModelTest()
             Assert.Equal(0.58252427184466016, metrics.PositivePrecision, 3);
             Assert.Equal(0.759493670886076, metrics.PositiveRecall);
 
-            ConfusionMatrix matrix = metrics.ConfusionMatrix;
+            var matrix = metrics.ConfusionMatrix;
             Assert.Equal(2, matrix.Order);
             Assert.Equal(2, matrix.ClassNames.Count);
             Assert.Equal("positive", matrix.ClassNames[0]);
@@ -432,7 +152,8 @@ public void CrossValidateSentimentModelTest()
             Assert.Equal(13, matrix[1, 1]);
             Assert.Equal(13, matrix["negative", "negative"]);
 
-            IEnumerable<SentimentPrediction> predictions  = cv.PredictorModels[0].Predict(sentiments);
+            var sentiments = GetTestData();
+            var predictions = cv.PredictorModels[0].Predict(sentiments);
             Assert.Equal(2, predictions.Count());
             Assert.True(predictions.ElementAt(0).Sentiment.IsTrue);
             Assert.True(predictions.ElementAt(1).Sentiment.IsTrue);
@@ -443,6 +164,138 @@ public void CrossValidateSentimentModelTest()
             Assert.True(predictions.ElementAt(1).Sentiment.IsTrue);
         }
 
+        private void ValidateBinaryMetrics(BinaryClassificationMetrics metrics)
+        {
+            Assert.Equal(.5556, metrics.Accuracy, 4);
+            Assert.Equal(.8, metrics.Auc, 1);
+            Assert.Equal(.87, metrics.Auprc, 2);
+            Assert.Equal(1, metrics.Entropy, 3);
+            Assert.Equal(.6923, metrics.F1Score, 4);
+            Assert.Equal(.969, metrics.LogLoss, 3);
+            Assert.Equal(3.083, metrics.LogLossReduction, 3);
+            Assert.Equal(1, metrics.NegativePrecision, 3);
+            Assert.Equal(.111, metrics.NegativeRecall, 3);
+            Assert.Equal(.529, metrics.PositivePrecision, 3);
+            Assert.Equal(1, metrics.PositiveRecall);
+
+            var matrix = metrics.ConfusionMatrix;
+            Assert.Equal(2, matrix.Order);
+            Assert.Equal(2, matrix.ClassNames.Count);
+            Assert.Equal("positive", matrix.ClassNames[0]);
+            Assert.Equal("negative", matrix.ClassNames[1]);
+
+            Assert.Equal(9, matrix[0, 0]);
+            Assert.Equal(9, matrix["positive", "positive"]);
+            Assert.Equal(0, matrix[0, 1]);
+            Assert.Equal(0, matrix["positive", "negative"]);
+
+            Assert.Equal(8, matrix[1, 0]);
+            Assert.Equal(8, matrix["negative", "positive"]);
+            Assert.Equal(1, matrix[1, 1]);
+            Assert.Equal(1, matrix["negative", "negative"]);
+        }
+
+        private LearningPipeline PreparePipeline()
+        {
+            var dataPath = GetDataPath(SentimentDataPath);
+            var pipeline = new LearningPipeline();
+
+            pipeline.Add(new Data.TextLoader(dataPath)
+            {
+                Arguments = new TextLoaderArguments
+                {
+                    Separator = new[] { '\t' },
+                    HasHeader = true,
+                    Column = new[]
+                    {
+                        new TextLoaderColumn()
+                        {
+                            Name = "Label",
+                            Source = new [] { new TextLoaderRange(0) },
+                            Type = Runtime.Data.DataKind.Num
+                        },
+
+                        new TextLoaderColumn()
+                        {
+                            Name = "SentimentText",
+                            Source = new [] { new TextLoaderRange(1) },
+                            Type = Runtime.Data.DataKind.Text
+                        }
+                    }
+                }
+            });
+
+            pipeline.Add(new TextFeaturizer("Features", "SentimentText")
+            {
+                KeepDiacritics = false,
+                KeepPunctuations = false,
+                TextCase = TextNormalizerTransformCaseNormalizationMode.Lower,
+                OutputTokens = true,
+                StopWordsRemover = new PredefinedStopWordsRemover(),
+                VectorNormalizer = TextTransformTextNormKind.L2,
+                CharFeatureExtractor = new NGramNgramExtractor() { NgramLength = 3, AllLengths = false },
+                WordFeatureExtractor = new NGramNgramExtractor() { NgramLength = 2, AllLengths = true }
+            });
+
+            pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2 });
+            pipeline.Add(new PredictedLabelColumnOriginalValueConverter() { PredictedLabelColumn = "PredictedLabel" });
+            return pipeline;
+        }
+
+        private void ValidateExamples(PredictionModel<SentimentData, SentimentPrediction> model)
+        {
+            var sentiments = GetTestData();
+            var predictions = model.Predict(sentiments);
+            Assert.Equal(2, predictions.Count());
+            Assert.True(predictions.ElementAt(0).Sentiment.IsFalse);
+            Assert.True(predictions.ElementAt(1).Sentiment.IsTrue);
+        }
+
+        private Data.TextLoader PrepareTextLoaderTestData()
+        {
+            var testDataPath = GetDataPath(SentimentTestPath);
+            var testData = new Data.TextLoader(testDataPath)
+            {
+                Arguments = new TextLoaderArguments
+                {
+                    Separator = new[] { '\t' },
+                    HasHeader = true,
+                    Column = new[]
+                    {
+                        new TextLoaderColumn()
+                        {
+                            Name = "Label",
+                            Source = new [] { new TextLoaderRange(0) },
+                            Type = Runtime.Data.DataKind.Num
+                        },
+
+                        new TextLoaderColumn()
+                        {
+                            Name = "SentimentText",
+                            Source = new [] { new TextLoaderRange(1) },
+                            Type = Runtime.Data.DataKind.Text
+                        }
+                    }
+                }
+            };
+            return testData;
+        }
+
+        private IEnumerable<SentimentData> GetTestData()
+        {
+            return new[]
+                        {
+                new SentimentData
+                {
+                    SentimentText = "Please refrain from adding nonsense to Wikipedia."
+                },
+                new SentimentData
+                {
+                    SentimentText = "He is a CHEATER, and the article should say that."
+                }
+            };
+        }
+
         public class SentimentData
         {
             [Column(ordinal: "0", name: "Label")]