Adding Temporal Dimension to Ontology Learning Models for Depression Signs Detection from Social Media Texts

Patricia Martin-Rodilla

2020

Abstract

Approaches to early detection of depression based on individual’s language are receiving increasing attention, with detection software systems based on lexical, grammatical or discursive components applied to medical corpus or social media texts. However, these first detection systems are defragmented, each attending to a specific feature or linguistic level, and not addressing a more conceptual level. Existing ontology learning (OL) methods extract the ontology referred in the text. In addition, existing systems perform language analysis for the detection of depression as a snapshot of each individual, regardless of their temporal dimension. Is it possible that suitable linguistic features to detect early signs of depression vary over time? And the underlying ontology? This paper presents a model that adds the temporal component to current ontology learning models to perform evolutionary analysis of both linguistic and ontological features to texts from social networks. The model has been applied to an external corpus of depression from social media texts, with a two-fold goal: 1) validating the model by contrasting it with OL models without temporal component 2) producing a corpus of evolutionary OL results applied to the depression detection from social media texts.

Paper Citation

in Harvard Style

Martin-Rodilla P. (2020). Adding Temporal Dimension to Ontology Learning Models for Depression Signs Detection from Social Media Texts.In Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-421-3, pages 323-330. DOI: 10.5220/0009351903230330

in Bibtex Style

@conference{enase20,
author={Patricia Martin-Rodilla},
title={Adding Temporal Dimension to Ontology Learning Models for Depression Signs Detection from Social Media Texts},
booktitle={Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2020},
pages={323-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009351903230330},
isbn={978-989-758-421-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Adding Temporal Dimension to Ontology Learning Models for Depression Signs Detection from Social Media Texts
SN - 978-989-758-421-3
AU - Martin-Rodilla P.
PY - 2020
SP - 323
EP - 330
DO - 10.5220/0009351903230330