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This study developed a natural language processing tool to classify depression in religious individuals using social media text analysis. Ontologies were created and compared to existing depression research.

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depression detectiondigital healthnatural language processingontology generationreligious communities

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Area of Science:

  • Mental Health Research
  • Computational Linguistics
  • Social Media Analysis

Background:

  • Depression is a prevalent mental disorder with significant individual and societal impacts.
  • Limited access to treatment and reluctance to seek help are major challenges in managing depression.
  • Online platforms offer anonymous spaces for support and discussion, generating valuable data for research.

Purpose of the Study:

  • To classify depression within a religious community by analyzing social media texts.
  • To develop a natural language processing (NLP) tool for depression classification.
  • To create a corpus for ontology development related to depression.

Main Methods:

  • Utilized semantic techniques, including comparative text analysis with ontologies.
  • Applied NLP for depression classification to build a specialized corpus.
  • Conducted qualitative and quantitative comparisons of generated ontologies.

Main Results:

  • Developed ontologies specific to depression in a religious community.
  • Compared these ontologies with each other and with existing general depression ontologies.
  • Quantitative analysis included similarity ratio calculations.

Conclusions:

  • The study successfully classified depression in a religious community using social media data and NLP.
  • Created novel ontologies that can be compared to existing depression research.
  • The developed NLP tool and methodologies contribute to understanding depression in specific populations.