Enhancing Suicide Prevention Strategies Through Real-Time Sentiment Analysis Using Machine Learning

Abstract

Suicide is a severe health concern in the community, and it is frequently aggravated by the fact that emotional distress remains unidentified at an early stage in online space. People often post their ideas and emotional states on the Internet, in their chats, and other online spheres and do not realize these danger signals. The given paper describes a machine-learning-based real-time sentiment analysis system, which will be used to detect the emotional state of depression, anxiety, and suicidal ideation through textual information. Sentiment classification and suicide risk prediction were performed with the help of Natural Language Processing methods and supervised machine learning models. The raw data were preprocessed and converted into an aspect that could be used in training the models. As the results of the experiments revealed, the proposed system exhibited the accuracy of 89, precision of 0.89, recall of 0.86, and F1-score of 0.86, meaning that its performance was rather stable and consistent. The system was effective in the detection of high-risk cases and creating alerts that aid in timely caregiver and mental health intervention. In sum, the suggested solution will offer a viable solution towards real-time mental health surveillance and preventing early suicide.