Mapping Phenomena Relevant to Adolescent Emotion Regulation: A Text-Mining Systematic Review

Caspar J. van Lissa*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

Adolescence is a developmentally sensitive period for emotion regulation with potentially lifelong implications for mental health and well-being. Although substantial empirical research has addressed this topic, the literature is fragmented across subdisciplines, and an overarching theoretical framework is lacking. The first step toward constructing a unifying framework is identifying relevant phenomena. This systematic review of 6305 articles used text mining to identify phenomena relevant to adolescents’ emotion regulation. First, a baseline was established of relevant phenomena discussed in theory and recent narrative reviews. Then, article keywords and abstracts were analyzed using text mining, examining term frequency as an indicator of relevance and term co-occurrence as an indicator of association. The results reflected themes commonly featured in theory and narrative reviews, such as socialization and neurocognitive development, but also identified undertheorized themes, such as developmental disorders, physical health, external stressors, structural disadvantage, substance use, identity and moral development, and sexual development. The findings illustrate how text mining systematic reviews, a novel approach, may complement narrative reviews. Future theoretical work might integrate these undertheorized themes into an overarching framework, and empirical research might consider them as promising areas for future research, or as potential confounders in research on adolescents’ emotion regulation.

Original languageEnglish
Pages (from-to)127–139
Number of pages13
JournalAdolescent Research Review
Volume7
Issue number1
Early online date21 May 2021
DOIs
Publication statusPublished - Mar 2022

Keywords

  • Adolescence
  • Emotion regulation
  • Machine learning
  • Systematic review
  • Text mining

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