Detection of Tendency to Depression through Text Analysis

Authors

  • Lauro Reyes-Cocoletzi Instituto Politécnico Nacional
  • J. Alejandro Aldama-Ramos
  • Alan Elias-Zapata
  • Jorge Betancourt-González
  • Jésus Rojas-Hernández

DOI:

https://doi.org/10.13053/cys-29-3-5887

Keywords:

Linguistic patterns, BERT, tendencies, depression, fine-tuning

Abstract

A project is proposed with the objectiveof detecting tendencies toward depression throughtext analysis, using Natural Language Processingtechnologies and Large Language Models (LLM). Thedevelopment included several phases, such as theselection and preprocessing of English transcripts fromthe low-resource Distress Analysis Interview Corpus Wizard of Oz (DAIC-WOZ) dataset [18, 19], as well asthe training of models based on Transformer architectures, specifically Bidirectional Encoder Representationsfrom Transformers (BERT), Robustly Optimized BERTApproach (RoBERTa), and Decoding-enhanced BERTwith Disentangled Attention (DeBERTa). The resultshighlight the performance of the BERT fine-tuningmodel, which achieved better metrics compared to theother architectures evaluated (RoBERTa and DeBERTafine-tuning models), with an average F1 score of0.76 and a consistently high Receiver OperatingCharacteristic – Area Under the Curve (ROC-AUC) value> 0.82. This demonstrates its ability to balance precisionand sensitivity, as well as identify linguistic patternsassociated with depressive symptoms.

Downloads

Published

2025-09-25

Issue

Vol. 29 No. 3 (2025): Vol 29 No 3 (2025)

Section

Articles of the Thematic Section

License 

Hereby I transfer exclusively to the Journal "Computación y 
Sistemas", published by the Computing Research Center (CIC-IPN),
the Copyright of the aforementioned paper. I also accept that these 
rights will not be transferred to any other publication, in any other 
format, language or other existing means of developing.
I certify that the paper has not been previously disclosed or simultaneo
usly submitted to any other publication, and that it does not contain 
material whose publication would violate the Copyright or other 
proprietary rights of any person, company or institution. I certify that 
I have the permission from the institution or company where I work or 
study to publish this work.
The representative author accepts the responsibility for the publication
of this paper on behalf of each and every one of the authors. 
This transfer is subject to the following conditions:
  • The authors retain all ownership rights (such as patent rights) of this work, except for the publishing rights transferred to the CIC, through this document.
  • Authors retain the right to publish the work in whole or in part in any book they are the authors or publishers. They can also make use of this work in conferences, courses, personal web pages, and so on.
  • Authors may include working as part of his thesis, for non-profit distribution only.