@article{ref1,
title="Benchmarking different classification techniques to identify depression patterns in an audio and text dataset",
journal="Webology",
year="2022",
author="Jaramillo-Valbuena, Sonia and Sánchez-Pineda, Cristian-Giovanny and Cardona-Torres, Sergio-Augusto",
volume="19",
number="6",
pages="1029-1038",
abstract="Depression is a health disorder that affects the population, regardless of their age or social status. The World Health Organization (WHO), considers it the greatest generator of incapacity worldwide. Depression increases the possibility of suicide, being the latter, the second trigger of death in people between fifteen and twenty-nine years of age. It negatively impacts different levels of the person: family, work and school and affects its ability to face daily life, aggravated preexisting medical conditions. Young or re-tired person and pregnant or postpartum women, are the groups most vulnerable to suffering from depressive disorder. In this paper, we apply two different classification techniques, namely: Bidirectional Encoder Representations from Transformers (BERT) and Support Vector Machines (SVM) in order to identify depression patterns in the Distress Analysis Interview Corpus DAIC-WOZ. We compare the models obtained and determine their robustness, using performance metrics. The results show that the approach BERT has good performance over the SVM model, reaching an accuracy of almost 90%.<p /> <p>Language: en</p>",
language="en",
issn="1735-188X",
doi="",
url="http://dx.doi.org/"
}