Abstract:
"This research endeavors to tackle the pressing issue of early identification of suicidal ideation in social media content, employing cutting-edge artificial intelligence and natural language processing techniques. With the exponential growth of social media usage, the need for automated systems capable of swiftly detecting signs of distress has become increasingly urgent. Recognizing the limitations of manual monitoring, this study aims to develop a sophisticated AI-driven solution capable of accurately discerning concerning content indicative of potential suicidal risk.
The methodology adopted in this study encompasses a multi-faceted approach, commencing with sentiment analysis to gauge the emotional tone of social media posts. Leveraging state-of-the-art Transformers BERT model, the system undertakes suicide prediction by analyzing linguistic nuances and contextual cues within the text. Subsequently, a novel multi-model concept is introduced to predict the severity level of suicidal ideation, leveraging advanced features extracted by Transformers BERT model. Each phase of the methodology involves meticulous data collection, pre-processing, and model training to ensure robust performance.
Initial findings from the implementation of the AI-driven system exhibit promising results, demonstrating high accuracy in detecting early signs of suicidal ideation and predicting the severity level of suicidal comments. Evaluation metrics such as precision, recall, and confusion matrices underscore the efficacy of the system, showcasing its potential to significantly augment suicide prevention efforts. However, further refinement and validation are imperative to optimize the system's performance across diverse social media platforms and user demographics, thereby maximizing its utility in real-world applications."
