Abstract

Suicide remains a global public health crisis, with millions expressing emotional distress and suicidal ideation on social media platforms, often without receiving timely intervention. This study explores the potential of Natural Language Processing (NLP) to detect suicide-related content by leveraging deep learning models trained on real-time user-generated text. Harnessing the power of language, the project aims to develop and benchmark cutting-edge NLP architectures capable of identifying suicidal ideation with high accuracy, precision, and recall. Unlike traditional clinical assessments, social media provides raw, unfiltered insight into individuals' mental states, offering a critical opportunity for early detection and preventive action. The research evaluates multiple deep learning models including Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM) networks, and hybrid CNN-LSTM architectures using labeled datasets from platforms such as Twitter and Reddit. Preprocessing techniques like tokenization, lemmatization, and noise reduction are applied to enhance semantic understanding. Furthermore, the study incorporates sentiment analysis, keyword extraction, and contextual embeddings to capture nuanced expressions of distress and intent. Performance is benchmarked across diverse metrics, including F1 score, Area Under the Curve (AUC), and Matthews Correlation Coefficient (MCC), with interpretability frameworks like LIME and SHAP used to ensure transparency in model predictions. The results demonstrate that contextual deep learning models significantly outperform traditional machine learning methods in detecting suicidal language patterns, even when disguised in metaphor, sarcasm, or indirect references. The study concludes by proposing an integrated framework for deploying these models in collaboration with mental health organizations, platform moderators, and emergency response services. Ethical considerations including user privacy, data anonymization, and false positive mitigation are emphasized to ensure responsible deployment. By bridging the gap between technology and mental health, this research underscores the transformative role of NLP in suicide prevention and advocates for AI-powered systems as a vital tool in safeguarding vulnerable populations in the digital age.