Analysis and Detection of Suicidal Ideation in Text Data Using Machine Learning

Research Background: Suicidal ideation is a major public health concern that affects millions of individuals worldwide and can lead to severe psychological consequences if not identified at an early stage. The increasing availability of user-generated textual content on social media and online platforms provides an opportunity to develop automated systems for detecting suicide-related risk factors through natural language analysis. Objective: This study aims to analyze and detect suicidal ideation in text data using machine learning techniques and to identify the most effective model for accurate, interpretable, and scalable mental health screening. Methods: A balanced dataset consisting of approximately 232,000 text posts obtained from Kaggle was utilized for experimentation. The proposed framework employed a four-stage pipeline involving data preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), model training, and evaluation. Three machine learning algorithms—Logistic Regression (LR), Linear Support Vector Machine (SVM), and Naive Bayes (NB)—were implemented and optimized using Grid Search CV. Performance was assessed using accuracy, precision, recall, F1-score, confusion matrix analysis, and cross-validation techniques. Results: The experimental results demonstrated that Logistic Regression achieved the best overall performance with an accuracy of 90.2% and an F1-score of 90.2%. Linear SVM achieved the highest recall of 92.1%, making it highly effective in identifying potential suicidal cases and reducing false negatives. Naive Bayes exhibited the fastest execution time while maintaining competitive classification performance. Feature importance analysis identified terms such as “hopeless†and “alone†as strong predictors of suicidal ideation. Conclusion: The findings indicate that traditional machine learning models can provide accurate, interpretable, and computationally efficient solutions for suicidal ideation detection. The proposed framework demonstrates significant potential for real-time mental health monitoring and suicide prevention systems. Future work will focus on integrating real-time data sources and conducting clinical validation studies to enhance practical deployment.