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 $\mathrm{Precision}$ of 0.665, $\mathrm{Recall}$ of 0.881, and ${\mathrm{F}}_1$-score of 0.758, coupled with superior latency metrics, including ${\mathrm{ERDE}}_5$ and ${\mathrm{ERDE}}_{50}$ at 0.025, ${\mathrm{Latency}}_{TP}$ at 1.0, and ${\mathrm{F}}_{latency}$ 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.

本文介绍了 TAM-SenticNet，这是一种神经符号人工智能框架，专为通过社交媒体内容分析检测早期抑郁症而设计。TAM-SenticNet 将用于特征提取和情感分析的神经网络与高级符号推理相结合，克服了传统诊断工具的局限性，特别是在实时响应性和可解释性方面。由 SenticNet 提供支持的符号推理提供了对情感表达的深入、结构化的理解，极大地增强了模型的可解释性和逻辑推理。实证评估表明，TAM-SenticNet 在性能指标方面优于现有模型，实现了$\mathrm{精确}$0.665，$\mathrm{记起}$0.881，以及${\mathrm{F}}_1$- 得分为 0.758，加上出色的延迟指标，包括${\mathrm{ERDE}}_5$和${\mathrm{ERDE}}_{50}$为 0.025，${\mathrm{潜伏}}_{\mathrm{时间}磷}$为 1.0，并且${\mathrm{F}}_{我AtenCy}$为 0.675。这些成就凸显了 TAM-SenticNet 在早期抑郁症检测方面的前沿方法，使其成为人工智能在心理健康信息学应用中的先锋工具。