The workflow shows how to use a Document Vector Adapter node in order to adjust the feature space of a second set of documents to make it identical to the feature space of a first, reference set of documents. It starts with reading textual data from a csv file and partitioning them into training and test data set. The sets are converted into documents, which are then preprocessed, i.e. filtered and stemmed and transformed into numerical document vectors. To make sure that the feature space of the test set is identical to the feature set of the training set, the Document Vector Applier node is applied. After the respective document vectors have been created the sentiment class is extracted and a predictive model is built and scored.