Mental Health via Writing
Open access
Peer reviewed
Abstract
Abstract
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 EMA =7,680). Text features were extracted using a dictionary-based approach, topic modeling, and GPT-derived emotion ratings. Random Forest and Elastic Net Regression models predicted NA from these text features, comparing nomothetic and idiographic approaches. All key findings, interactive visualizations, and model comparisons are available via a companion web app: https://emotracknlp.streamlit.app/. Idiographic models combining text features from different NLP approaches exhibited the best performance: they performed comparably to nomothetic models in R² but yielded lower prediction error (Root Mean Squared Error), improving within-person precision. Importantly, there were substantial between-person differences in model performance and predictive linguistic features. When selecting the best-performing model for each participant, significant correlations between predicted and observed emotion scores were found for 90.7–94.8% of participants. Our findings suggest that while nomothetic models offer initial scalability, idiographic models may provide greater predictive precision with sufficient within-person data. A flexible, personalized approach that selects the optimal model for each individual may enhance emotion monitoring, while leveraging text data to provide contextual insights that could inform appropriate interventions.
Direct answer
What can I do from this paper page?
Use this page to scan "Using natural language processing to track negative emotions in the daily lives of adolescents." quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Mental Health via Writing research, save the paper, or map adjacent work.
Research areas
Follow related topics
Citation
BibTeX
@article{Fisher2026Using,
title = {Using natural language processing to track negative emotions in the daily lives of adolescents.},
author = {Hadar Fisher and N Jaffe and Kristina Pidvirny and Anna O. Tierney and Diego A. Pizzagalli and Christian A. Webb},
journal = {Journal of Psychopathology and Clinical Science},
year = {2026},
doi = {10.1037/abn0001150},
url = {https://doi.org/10.1037/abn0001150}
}FAQ
Using this paper in a discovery workflow
How do I find related work for this paper?
Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.
How can I keep up with new Mental Health via Writing research papers?
Follow Mental Health via Writing research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.
Can I cite this paper from this page?
This page includes a static BibTeX block for Using natural language processing to track negative emotions in the daily lives of adolescents.. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.
Follow this research in Scollr
Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.
Get the app