Evidence map›Paper›PMID 42005017›Full record

ArticleFrontiers in psychology2026

Depression classification based on automatic ontology generation and natural language processing.

Nicolae Goga, Andrei Vasilăteanu, Ramona Cristina Popa, Alexandru-Filip Popovici, Ramona Popovici, Maria Goga, Diana Todea, Alexandra Eni

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Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Nicolae Goga *Department of Engineering in Foreign Languages, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania.
Andrei Vasilăteanu *Department of Engineering in Foreign Languages, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania.
Ramona Cristina Popa *Department of Engineering in Foreign Languages, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania.
Alexandru-Filip Popovici *Faculty of Psychology and Educational Sciences, University of Bucharest, Bucharest, Romania.
Ramona Popovici *Doctoral School of Psychology and Educational Sciences, University of Bucharest, Bucharest, Romania.
Maria Goga *Department for the Training of Teaching Staff, Technical University of Civil Engineering of Bucharest, Bucharest, Romania.
Diana TodeaInterdisciplinary School of Doctoral Studies, University of Bucharest, Bucharest, Romania.
Alexandra EniDepartment of Psychology and Cognitive Sciences, University of Bucharest, Bucharest, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Depression is one of the most common mental disorders and one that has a great potential to affect people mentally, physically, and socially. Unfortunately, a majority of people either do not have access to treatment or avoid seeking help. In this context, many platforms have emerged to provide a space for discussion and support where users can interact anonymously. Methods: This study presents the results of our research on classifying depression in a specific community of religious people by analyzing texts posted on social media using semantic techniques, such as a comparative analysis of texts from users using ontologies. A supporting objective of the research was to create a natural language processing tool for classifying depression to obtain the corpus necessary for ontology creation. Results: The resulting ontologies were analyzed and compared with each other and also with existing ontologies in the literature on general depression. Discussion: The comparison was both qualitative and quantitative, taking into consideration similarity ratios for the quantitative comparison.

Indexed as

depression detectiondigital healthnatural language processingontology generationreligious communities

Identifiers

PMID42005017
PMCPMC13083004

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.