StyloMetrix: An Open-Source Multilingual Tool for Representing
  Stylometric Vectors
Inez Okulska Daria Stetsenko Anna Kołos Agnieszka Karlińska Kinga Głąbińska Adam Nowakowski
This work aims to provide an overview on the open-source multilanguage tool
called StyloMetrix. It offers stylometric text representations that cover
various aspects of grammar, syntax and lexicon. StyloMetrix covers four
languages: Polish as the primary language, English, Ukrainian and Russian. The
normalized output of each feature can become a fruitful course for machine
learning models and a valuable addition to the embeddings layer for any deep
learning algorithm. We strive to provide a concise, but exhaustive overview on
the application of the StyloMetrix vectors as well as explain the sets of the
developed linguistic features. The experiments have shown promising results in
supervised content classification with simple algorithms as Random Forest
Classifier, Voting Classifier, Logistic Regression and others. The deep
learning assessments have unveiled the usefulness of the StyloMetrix vectors at
enhancing an embedding layer extracted from Transformer architectures. The
StyloMetrix has proven itself to be a formidable source for the machine
learning and deep learning algorithms to execute different classification
tasks.