A comprehensive review of detecting hidden distress using machine learning and sensor fusion in digital space

Distress management and digital wellbeing have become a critical focus within affective computing, driven by the growing prevalence of digital communication across social media platforms. Conventional methods of sentiment evaluation techniques fail to apprehend the complexity of human emotions. This problem becomes particularly more difficult when conveyed through informal and context-rich textual interactions on social media. To address these limitations, the amalgamation of machine learning methods with sensor-based physiological data offers a robust multimodal framework for emotion and stress recognition. This study focuses on recent progressions in emotion and stress detection methodologies, particularly in scenarios involving social media contents enriched with contextual hardware data such as voice signals, facial expressions, and physiological indicators. We analyze the application of state-of-the-art machine learning methods, including deep neural networks and transformer-based architectures, alongside contributions from wearable and embedded sensor systems. The study also discusses existing challenges such as data variability, sensor noise, and annotation constraints, while highlighting practical applications in mental health monitoring, human-computer interaction, and intelligent user modeling. Finally, future research directions are captured in order to enhance multimodal human mood recognition through cross-modal fusion, real-time adaptability, and privacy-aware deployment strategies.