ArticleResearch square2025
Using Natural Language Processing to Track Negative Emotions in the Daily Lives of Adolescents.
Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Tracking emotion fluctuations in adolescents' daily lives is essential for understanding mood dynamics and identifying early markers of affective disorders. This study examines the potential of text-based approaches for emotion prediction by comparing nomothetic (group-level) and idiographic (individualized) models in predicting adolescents' daily negative affect (NA) from text features. Additionally, we evaluate different Natural Language Processing (NLP) techniques for capturing within-person emotion fluctuations. We analyzed ecological momentary assessment (EMA) text responses from 97 adolescents (ages 14-18, 77.3% female, 22.7% male, N
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