Study of NLP Models and Their Efficacy in Detection of Early Signs of Depression and Suicidal Tendencies


  • Atman Shastri Research Scholar, Department of Computer Applications, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, India.


Natural Language Processing, NLP Models, Depression, Suicidal Tendencies, Linguistic Patterns, Mental Health, BERT, GPT-3, XLNet, Early Detection, Textual Analysis, Social Media, Forums, Clinical notes, Ethical Considerations, Privacy Preservation, Bias Mitigation


This research examines the potential of Natural Language Processing (NLP) techniques, particularly BERT, GPT-3, and XLNet, to identify early signs of depression and suicidal tendencies by analysing written text. The study investigates the ability of these NLP models to detect linguistic patterns associated with mental health issues. The main concept is to look for language patterns in written text—such as blog posts, online conversations, or even creative writing—that are connected to mental health issues. Consider a social media site that uses natural language processing (NLP) to identify posts that may be troubling and discreetly link users to mental health resources. These models could be used by online treatment platforms to identify people who want urgent assistance and to personalise sessions. The paper acknowledges the difficulties as well; the possibility of bias in the training set of NLP models is one thing to be concerned about. Furthermore, written language may be imprecise, leading to misunderstandings of sarcasm or metaphorical speaking. The research suggests using fundamental NLP models to address this and give a more in-depth comprehension of the text’s emotional undertones. This study expands on the foundation of sentiment analysis by utilising the capabilities of the BERT, GPT-3, and XLNet models. These sophisticated NLP techniques are capable of capturing the complex language patterns linked to suicidal thoughts and depression. We go beyond general sentiment analysis to spot minute changes in language usage, such as an increase in words associated with negative emotions or a decrease in language complexity. The goal of this research is to use text analysis from multiple sources to create an early warning system for mental health emergencies. The possible impact is in providing timely help and intervention to individuals in need, which could eventually result in lifesaving. We use precision, recall, and F1-score to statistically evaluate the effectiveness of XLNet, GPT-3, and BERT in text-based depression identification.


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