Leveraging Emotional Traces For Automatic Identification Of Suicidal Ideation In Text

Abstract

Suicide is a critical mental health concern and a leading cause of death globally. Emotional dysregulation has been widely recognized as a key factor contributing to suicidal behavior. In the digital age, individuals increasingly express suicidal ideation on social media platforms, often seeking help, empathy, or validation. This study introduces a natural language processing (NLP)- driven framework to classify suicide notes based on emotional content, utilizing deep learning techniques for enhanced detection. By incorporating advanced NLP preprocessing techniques— such as tokenization, lemmatization, stop word removal, and word embeddings (e.g., Word2Vec or GloVe)—the model is able to effectively extract semantic and emotional features from unstructured text. To analyze these emotional patterns, we employ a Long Short-Term Memory (LSTM) neural network, capable of capturing temporal dependencies and sequential sentiment shifts within the text. The LSTM model is trained to recognize latent emotional states that correlate with suicidal ideation, enabling binary classification of notes as either suicidal or non-suicidal. Our approach highlights the linguistic subtleties in suicide-related content, particularly the differences in vocabulary between social media posts and authentic suicide notes. The findings suggest that NLP-enhanced LSTM models can significantly improve the ability to detect indirect or emotionally nuanced indicators of suicidal intent, offering a promising tool for early intervention and mental health monitoring.