Narrative complexity is weakly coupled with affective energy, not net valence, in relationship narratives

People often assume that emotionally intense relationship experiences “leak” into language: more elaborated stories should sound more emotional. We tested this expressive-congruence assumption in 351,734 de-identified relationship narratives using an interpretable index of narrative complexity (Level of Complexity; LoC) and sentence-level sentiment summaries. LoC showed little association with net valence magnitude (the absolute value of the document-level mean sentence valence, v i ; r = − 0.061 , R 2 = 0.37 % ) but a modest positive association with cancellation-robust affective intensity (MAV: r = 0.186 , R 2 = 3.47 % ; RMS: r = 0.220 , R 2 = 4.85 % ). A residual-based discrepancy index (NADI, defined on net valence magnitude) revealed systematic expressive profiles, including highly elaborated but affectively muted narratives and brief yet affectively charged disclosures. Unsupervised clustering yielded five reproducible expressive profiles that showed structural parallels with constructs discussed in emotion-regulation and self-presentation research, without providing direct evidence of subjective regulatory intent. Because the design is correlational, these associations do not establish causal relations or individual-level regulatory processes. Narrative elaboration is therefore not a reliable proxy for surface-level net affective tone in naturalistic relational self-disclosure.

Authors

Institutions

Publication Details

Journal
Acta Psychologica
Published
2026-08-27
DOI
https://doi.org/10.1016/j.actpsy.2026.107689
Primary Topic
Attachment and Relationship Dynamics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Narrative complexity is weakly coupled with affective energy, not net valence, in relationship narratives

Ryan SangBaek Kim
Acta Psychologica
Attachment and Relationship Dynamics
article

Narrative complexity is weakly coupled with affective energy, not net valence, in relationship narratives

Ryan SangBaek Kim
article en

Abstract

People often assume that emotionally intense relationship experiences “leak” into language: more elaborated stories should sound more emotional. We tested this expressive-congruence assumption in 351,734 de-identified relationship narratives using an interpretable index of narrative complexity (Level of Complexity; LoC) and sentence-level sentiment summaries. LoC showed little association with net valence magnitude (the absolute value of the document-level mean sentence valence, v i ; r = − 0.061 , R 2 = 0.37 % ) but a modest positive association with cancellation-robust affective intensity (MAV: r = 0.186 , R 2 = 3.47 % ; RMS: r = 0.220 , R 2 = 4.85 % ). A residual-based discrepancy index (NADI, defined on net valence magnitude) revealed systematic expressive profiles, including highly elaborated but affectively muted narratives and brief yet affectively charged disclosures. Unsupervised clustering yielded five reproducible expressive profiles that showed structural parallels with constructs discussed in emotion-regulation and self-presentation research, without providing direct evidence of subjective regulatory intent. Because the design is correlational, these associations do not establish causal relations or individual-level regulatory processes. Narrative elaboration is therefore not a reliable proxy for surface-level net affective tone in naturalistic relational self-disclosure.

Acta PsychologicaVol. 270
People's Hospital of Shiyan (CN), Clinique Claude-Bernard (FR)
Openalex Percentile: Top 6%
Attachment and Relationship Dynamics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.