Feature engineering is an important step in classical NLP pipelines, but machine learning engineers may not be aware of the signals to look for when processing foreign language text. The Russian Feature Extraction Toolkit (RFET) is a collection of fe
ature extraction libraries bundled for ease of use by engineers who do not speak Russian. RFET's current feature set includes features applicable to social media genres of text and to computational social science tasks. We demonstrate the effectiveness of the tool by using it in a personality trait identification task. We compare the performance of Support Vector Machines (SVMs) trained with and without the features provided by RFET; we also compare it to a SVM with neural embedding features generated by Sentence-BERT.
