Enhanced Suicidal Ideation Detection via Text Classification and Ensemble Methods

Suicidal ideation detection in textual data has emerged as a critical area of research, with implications for mental health support and intervention. This study presents a novel approach that integrates ensemble modeling and keyword-based categorization to improve the detection and analysis of suicidal tendencies in text. Using a labeled dataset with balanced class distribution (10,009 suicide and 9,988 non-suicide instances), we performed comprehensive preprocessing, including the removal of URLs, mentions, hashtags, emojis, stopwords, and applying lemmatization and tokenization. Features were extracted using CountVectorizer, and multiple machine learning models, including K-Nearest Neighbors (KNN), SVM, Naive Bayes, Decision Tree, Logistic Regression, and LSTM, were evaluated. An ensemble model combining KNN, Naive Bayes, and Decision Tree achieved the highest binary classification accuracy of 94.87%, demonstrating its superiority. Our novelty lies in the creation of a new dataset by leveraging the ensemble model to identify suicide-related text and categorizing entries into explicit, implicit, help-seeking, and emotional distress categories using domain-specific keywords. Multi-class classification on this enriched dataset was performed using advanced algorithms, including XGBoost, CatBoost, and LightGBM, with LightGBM achieving the highest 96.32% accuracy. This innovative integration of ensemble modeling and keyword-based categorization highlights the potential for enhancing suicidal ideation detection and mental health monitoring systems.