Hi Everyone 
A little bit late with this one but here is my solution for challenge 6:
The workflow uses the -Enrichment and Preprocessing- component, followed by the -Document Vectorization- component in order to prepare the data for training. An -X-Partitioner- node is used to start a cross validation loop and inside the loop are the -Logisitic Regression Learner- and the -Logistic Regression Predictor- nodes using “Class” as the target column and “neutral” as the reference category. The -Loop End- node concatenates the results from each iteration.
The -Scorer- calculates the accuracy at around 75%, which is acceptable for something that is not crucial and for the simplicity of the workflow used to create the model. However, it is also important to assess the model’s performance. In this solution there is high specificity for all 3 classes and reasonable to high sensitivity for the positive and negative classes, where as the neutral class has a sensitivity around 0.5 and is therefore pretty much random.
You can find my workflow on the hub here:
Best wishes
Heather