loading
Documents

Research.Publish.Connect.

Paper

Author: Patricia Martin-Rodilla

Affiliation: Information Retrieval Lab (IRLab), Facultade de Informática, University of A Coruña, Spain

ISBN: 978-989-758-421-3

Keyword(s): Ontology Learning, Time, Ontology Evolution, Text Mining, Social Media, Depression, Early Risk Prediction.

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. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.239.167.74

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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

@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},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.