Mood Swing Analysis

Abstract

Affective computing has emerged as a cornerstone of human-computer interaction (HCI), healthcare analytics, and digital mental health monitoring. Traditional emotion recognition frameworks heavily rely on unimodal architectures—analyzing either text logs, facial expressions, or acoustic patterns in isolation. However, unimodal systems are inherently prone to environmental noise, semantic ambiguities, and cross-channel context blindness, which restrict their real-world reliability. This paper presents a systematic review of contemporary advancements in automated mood swing analysis, focusing on the evolution from handcrafted unimodal classifiers to deep-learning-driven multimodal architectures. We dissect the structural components of feature extraction across linguistic, visual, and acoustic domains, evaluate Early, Late, and Hybrid fusion mechanics, and analyze the deployment bottlenecks in transitioning from complex, resource-intensive models to lightweight web-based frameworks. Finally, we highlight critical gaps in current literature, particularly regarding the handling of cross-modal emotional inconsistencies and real-world framework deployments.