The study is focused on the detection of depression by processing and classification of short essays written by 316 volunteers. The set of 93 essays was provided by two different teams of psychologists who asked patients with clinically confirmed depression to write short essays on the neutral topic. The other 223 essays on the same topic were written by volunteers who completed questionnaires, which are designed to reveal depression status and did not demonstrate any signs of mental illnesses. The study describes psycholinguistic and classic text features which were calculated by utilizing natural language processing tools and were used to perform on the classification task. The machine learning classification models achieved up to 73% of f1-score for the task of revealing essays written by people with depression.
PDF at the Dialogue international conference website: www.dialog-21.ru/media/4629/stankevichmaplusetal-125.pdf
RUDN University. Repository: https://repository.rudn.ru/en/records/article/record/65816/
Semantic Scholar: https://api.semanticscholar.org/CorpusID:219602004
Stankevich M., Smirnov I., Kuznetsova Y., Kiselnikova N., Enikolopov S. Predicting Depression from Essays in Russian. Proceedings of “Computational Linguistics and Intellectual Technologies” DIALOGUE. - 2019. - №. 18. - Pp. 637-647