Using natural language processing analysis to investigate daily-life positive autobiographical memories
Abstract
Recalling positive autobiographical memories (AMs) can improve wellbeing, yet few studies capture these memories as they naturally occur or examine their detailed content. In a sample of 70 community adults, we used ecological momentary assessment to assess naturally occurring positive AMs in daily life. Using the large language model-based Python library BERTopic, we identified eight underlying topics: ‘Friends, partner, family’ (68.42%), ‘Work, study’ (8.33%), ‘Being in nature or the moment, pets’ (5.60%), ‘Family & social events’ (3.69%), ‘Exam success’ (3.96%), ‘Success in sports – self or as a fan’ (3.28%), ‘Outdoor activities and sports’ (3.14%), and ‘School days, studies’ (2.19%). Participants reported vivid, sensory AMs accompanied by positive emotions. Greater AM vividness was systematically associated with greater immediate AM-related happiness, underscoring vividness as a potential amplifier in everyday remembering. By sharing our detailed methodology, code, and anonymized examples, we lay the groundwork for large-scale computational analysis of AMs assessed under the noisy and unstructured conditions of everyday life.
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BibTeX
@misc{haag2026using,
title = {{Using natural language processing analysis to investigate daily-life positive autobiographical memories}},
author = {Christina Haag and Lynn Alison Bueeler and David Bürgin and Michel Philipp and Birgit Kleim},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/5tjpg_v2},
url = {https://osf.io/preprints/psyarxiv/5tjpg_v2/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Using natural language processing analysis to investigate daily-life positive autobiographical memories
AU - Christina Haag
AU - Lynn Alison Bueeler
AU - David Bürgin
AU - Michel Philipp
AU - Birgit Kleim
PY - 2026
DA - 2026/09/23
PB - PsyArXiv
DO - 10.31234/osf.io/5tjpg_v2
UR - https://osf.io/preprints/psyarxiv/5tjpg_v2/
AB - Recalling positive autobiographical memories (AMs) can improve wellbeing, yet few studies capture these memories as they naturally occur or examine their detailed content. In a sample of 70 community adults, we used ecological momentary assessment to assess naturally occurring positive AMs in daily life. Using the large language model-based Python library BERTopic, we identified eight underlying topics: ‘Friends, partner, family’ (68.42%), ‘Work, study’ (8.33%), ‘Being in nature or the moment, pets’ (5.60%), ‘Family & social events’ (3.69%), ‘Exam success’ (3.96%), ‘Success in sports – self or as a fan’ (3.28%), ‘Outdoor activities and sports’ (3.14%), and ‘School days, studies’ (2.19%). Participants reported vivid, sensory AMs accompanied by positive emotions. Greater AM vividness was systematically associated with greater immediate AM-related happiness, underscoring vividness as a potential amplifier in everyday remembering. By sharing our detailed methodology, code, and anonymized examples, we lay the groundwork for large-scale computational analysis of AMs assessed under the noisy and unstructured conditions of everyday life.
ER -