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Authors: Maxim Stankevich ; Vadim Isakov ; Dmitry Devyatkin and Ivan Smirnov

Affiliation: Federal Research Center ”Computer Science and Control” of RAS, Russian Federation

ISBN: 978-989-758-276-9

Keyword(s): Depresion Detection, Social Media, Classification, Machine Learning.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Classification ; Feature Selection and Extraction ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Natural Language Processing ; Pattern Recognition ; Symbolic Systems ; Theory and Methods

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.

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Paper citation in several formats:
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

@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},
}

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

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