SocioMind AI is an AI-powered analytical framework that quantifies psychological states and personality traits through the automated processing of heterogeneous social media data. Unlike traditional sentiment analysis — which reduces complex human communication to a single positive/negative polarity score — SocioMind AI employs a multi-dimensional approach to construct a comprehensive \\\"Linguistic DNA\\\" profile of an individual, correlating public persona signals with private aspirational data to deliver a 360-degree behavioral footprint. The system operationalizes a novel concept: the Digital Behavioral Footprint (DBF) — the aggregate, cross-contextual trace that an individual leaves across multiple social media channels, each reflecting a different facet of their psychological identity. By processing and cross-referencing Primary Content, Interactional Tone, Interest Graphs, and Aspirational Signals simultaneously, SocioMind AI achieves what single-channel sentiment tools cannot: a holistic, internally-validated psychological portrait. At its inference core, SocioMind AI leverages the Gemini 3 Flash large language model architecture, optimized for structured JSON output to ensure deterministic, research-grade data handling. The analytical output spans Big Five personality trait quantification, Emotional Density Mapping, and derived psychological indicators including Social Stress Levels, Behavioral Consistency scores, and Mood Trajectory projections. The system is implemented as a React-based web application with Recharts-powered radar and bar chart visualizations, making complex psychological matrices accessible to both researchers and non-specialist users. Validation experiments across 300 profiles demonstrate Cohen\\\'s kappa = 0.74 for Big Five dimensions and Pearson r = 0.81 for emotional valence detection, establishing SocioMind AI as a viable zero-knowledge psychological proxy for research-grade personality inference.
Udit Tripathi (Thu,) studied this question.