Interpretable Socioeconomic Profiling: A Deep Dive Beyond Narrative Classification

This work presents an interpretable framework for socioeconomic status (SES) profiling based on narrative data. Building on our previous publication, “AI Assistant for Socioeconomic Empowerment Using Federated Learning” (NLP4DH 2025), this extended study explores a complementary system that focuses on thematic topic modeling, transformer-based embedding comparisons, and visualization tools. The framework analyzes student and public narratives to detect SES-related themes (e.g., financial hardship, resilience, access to resources) and assigns SES profiles through similarity-based scoring. By emphasizing interpretability and topic-based filtering, the system facilitates analysis of language patterns linked to different SES groups while supporting qualitative inspection. Results demonstrate the model’s ability to generalize across diverse domains and align with known social science frameworks, contributing toward responsible and transparent AI in education and public policy contexts.