TAM-SenticNet: A Neuro-Symbolic AI approach for early depression detection via social media analysis

This paper introduces TAM-SenticNet, a Neuro-Symbolic AI framework uniquely designed for early depression detection through social media content analysis. Merging neural networks for feature extraction and sentiment analysis with advanced symbolic reasoning, TAM-SenticNet overcomes the limitations of traditional diagnostic tools, particularly in real-time responsiveness and interpretability. The symbolic reasoning, powered by SenticNet, provides a deep, structured understanding of emotional expressions, greatly enhancing model explainability and logical inference. Empirical evaluations reveal that TAM-SenticNet excels beyond existing models in performance metrics, achieving a Precision of 0.665, Recall of 0.881, and F1 -score of 0.758, coupled with superior latency metrics, including ERDE5 and ERDE50 at 0.025, LatencyTp at 1.0, and Fh �������������,,,., at 0.675. These achievements highlight TAM-SenticNet's cutting -edge approach to early depression detection, making it a pioneering tool in the application of AI for mental health informatics.