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.
Keywords:
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:
