Feature Engineering for Depression Detection in Social Media

Maxim Stankevich, Vadim Isakov, Dmitry Devyatkin, Ivan Smirnov

2018

Abstract

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.

Paper Citation

in Harvard Style

Stankevich M., Isakov V., Devyatkin D. and Smirnov I. (2018). Feature Engineering for Depression Detection in Social Media.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 426-431. DOI: 10.5220/0006598604260431

in Bibtex Style

@conference{icpram18,
author={Maxim Stankevich and Vadim Isakov and Dmitry Devyatkin and Ivan Smirnov},
title={Feature Engineering for Depression Detection in Social Media},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={426-431},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006598604260431},
isbn={978-989-758-276-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Feature Engineering for Depression Detection in Social Media
SN - 978-989-758-276-9
AU - Stankevich M.
AU - Isakov V.
AU - Devyatkin D.
AU - Smirnov I.
PY - 2018
SP - 426
EP - 431
DO - 10.5220/0006598604260431