International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Training a BERT-base NLP Model to Identify Cognitive Distortions and Understand their Intersection with Suicide-Risk

Author(s) Sreya Jampana
Country India
Abstract Cognitive distortions are irrational thoughts influencing mental health and are closely linked to self-harm. This paper explores the use of natural language processing (NLP) techniques to identify cognitive distortions and suicide risks in written text. Various models were tested, with BERT-base showing the highest accuracy of 53%. Cognitive distortions were inferred on a dataset of Reddit posts labelled as suicidal and non-suicidal, showing that distortions like overgeneralization and mental filtering are prevalent in suicidal texts. This research highlights the relationship between cognitive distortions and suicide risk, suggesting that certain distortions could be early indicators of suicidal ideation. Findings support using NLP models for mental health interventions, but future improvements, such as more training epochs, multi-task models, and larger datasets, are needed to enhance accuracy and intervention efficacy.
Keywords Cognitive Distortions, Suicide-Risk, Natural Language Processing, BERT-base
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-10-25
Cite This Training a BERT-base NLP Model to Identify Cognitive Distortions and Understand their Intersection with Suicide-Risk - Sreya Jampana - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.29203
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.29203
Short DOI https://doi.org/g8pnnw
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