Language as a Lifeline: Leveraging NLP to Suicide Detection Through Context-Aware AI Models

Abstract

In an era where mental health challenges are surging globally, suicide remains one of the leading causes of death, claiming over 700,000 lives annually according to the World Health Organization. The rise of social media as a primary outlet for emotional expression offers a vital opportunity to detect early warning signs of suicide through automated analysis. This research investigates the application of advanced Natural Language Processing techniques to identify suicidal ideation in Reddit posts, an urgent, real-world use case with life-saving potential. A comparative analysis of three classification approaches is conducted: traditional Logistic Regression with TF-IDF, a deep learning-based LSTM model, and a transformer-based BERT model. Using a curated dataset of Reddit posts labeled for suicide risk, models were trained and evaluated using 5-fold cross-validation and standard metrics. Results indicate that BERT significantly outperforms both alternatives, achieving an accuracy of 94% and an F1-score of 93%, compared to 91% & 90% for LSTM and 87% & 85% for Logistic Regression. McNemar’s test further validates the statistical significance of BERT’s superiority (p < 0.05). This study highlights the transformative potential of context-aware language models in mental health detection. By integrating such systems into digital platforms, stakeholders, including clinicians, researchers, and tech companies, can enable real-time, scalable, and ethical monitoring of suicidal behavior. In a time when every signal matter, this work contributes a critical step toward AI-assisted mental health interventions.