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Feature Engineering for Depression Detection in Social Media - 2018

Feature Engineering For Depression Detection In Social Media

Research Paper on Feature Engineering For Depression Detection In Social Media

Research Area:  Machine Learning


This research is based on the CLEF/eRisk 2017 pilot task which is focused on early risk detection of depression. The CLEF/eRsik 2017 dataset consists of text examples collected from messages of 887 Reddit users. The main idea of the task is to classify users into two groups: risk case of depression and non-risk case. This paper considers different feature sets for depression detection task among Reddit users by text messages processing. We examine our bag-of-words, embedding and bigram models using the CLEF/eRisk 2017 dataset and evaluate the applicability of stylometric and morphological features. We also perform a comparison of our results with the CLEF/eRisk 2017 task report.

Feature Engineering
Depression Detection
Social Media
Machine Learning
Deep Learning

Author(s) Name:  Maxim Stankevich ; Vadim Isakov ; Dmitry Devyatkin and Ivan Smirnov

Journal name:  

Conferrence name:  Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM

Publisher name:  SCITEPRESS

DOI:  10.5220/0006598604260431

Volume Information: