Detecting Behavioral and Emotional Themes Through Latent and Explicit Knowledge

Social organizations increasingly rely on Natural Language Processing (NLP) to analyze large-scale textual data for high-stakes decisions, including university admissions, financial aid allocation, and job hiring. Current methods primarily employ topic modeling and sentiment analysis, but they fail to capture the complex ways emotions and cultural contexts shape meaning in text, potentially perpetuating bias and undermining equitable decision-making. To address this gap, we introduce the Behavioral and Emotional Theme Detection (BET) framework, a novel approach that integrates emotional, cultural, and sociological dimensions into topic detection and emotion analysis. By applying BET to English and Hebrew datasets, we showcase its multilingual adaptability and its potential to reveal rich thematic content and emotional resonance in biographical texts. Our results demonstrate that BET not only enhances the granularity and diversity of detected themes but also tracks shifts in emotional framing over time, offering deeper insights into how individuals deploy linguistic resources to position their identities, enabling more equitable assessment practices.