Go to ACL ARR 2025 February homepageDetecting Behavioral and Emotional Themes Through Latent and Explicit Knowledge
Abstract: Social science research increasingly employs Natural Language Processing (NLP) to analyze large-scale textual data, yet common methods like topic modeling and sentiment analysis often overlook the nuanced ways in which emotions and cultural contexts shape meaning. 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.
Paper Type: Long
Research Area: Special Theme (conference specific)
Research Area Keywords: sociolinguistics, emotion detection and analysis, NLP tools for social analysis
Contribution Types: Model analysis & interpretability, Publicly available software and/or pre-trained models, Data analysis
Languages Studied: English, Hebrew
Submission Number: 1282
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