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Detecting Emotional Signals of Relationship Well-Being Using AI: A Cross-Sectional Comparison of Transformer-Based and Lexicon-Based Models for Couples Therapy Research

Saavedra-Roa, Alejandro, Pineda-Marin, Claudia, Vallejo-Medina, Pablo, Montesano, Adrián·2026·JMIR AI
DOI 10.2196/94097
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Abstract

Background: Natural language processing and AI models are increasingly used to extract emotional signals from textual data; however, systematic validation of different computational paradigms in emotionally complex relational contexts remains limited. Such approaches may contribute to digital health research, particularly in the fields of digital mental health and couples therapy, where understanding emotional expressions within romantic relationships is especially relevant. Objective: This study aimed to compare a lexicon-based approach (NRC Emotion Lexicon) and a transformer-based model (Pysentimiento) in detecting affective patterns within spontaneous romantic relationship narratives and to evaluate their criterion-related associations with a theory-driven measure of relational well-being. Methods: In this cross-sectional exploratory study, 112 Spanish adults completed the Couple Flourishing Measure (CFM) and provided an open-ended description of their romantic relationship. Narratives were analyzed using both computational approaches to generate emotion and sentiment indices. Pearson correlations were computed between model outputs and CFM scores. Steiger Z tests were conducted to compare dependent correlations across models. Results: Both approaches showed significant associations with couple flourishing, although the transformer-based model yielded stronger correlations for several affective dimensions, particularly positive sentiment ( r =0.66, 95% CI 0.54-0.76) and negative sentiment ( r =−0.74, 95% CI −0.82 to −0.65). The lexicon-based approach showed smaller associations, primarily for negative emotions. Steiger Z tests identified significant between-model differences for disgust ( P =.04), joy ( P =.01), sadness ( P =.004), surprise ( P =.03), and positive and negative sentiment (both P <.001), but not for anger or fear. Conclusions: In this sample, Pysentimiento showed stronger associations with couple flourishing than the NRC Emotion Lexicon across several affective dimensions, although differences were not consistent across all categories. These findings support the value of theory-driven external criteria for evaluating computational emotion detection in relational narratives and underscore the importance of context-sensitive interpretation when applying AI tools in psychologically sensitive domains. These findings also highlight the potential relevance of computational emotion analysis for digital health research, particularly in digital mental health and couples therapy research, although further validation in clinical settings is needed before clinical implementation.

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Research works printed but not linked: 37

  1. Nadkarni, Natural language processing: an introduction, J Am Med Inform Assoc, 18, 544, 10.1136/amiajnl-2011-000464 · DOI
  2. Qiu, Pre-trained models for natural language processing: a survey, Sci China Technol Sci, 63, 1872, 10.1007/s11431-020-1647-3 · DOI · Paper page
  3. Chowdhury, Natural language processing, Annu Rev Inf Sci Technol, 37, 51, 10.1002/aris.1440370103 · DOI
  4. Vaswani A Shazeer N Parmar N et al. Attention is all you need. In: Guyon I Von Luxburg U Bengio S Wallach H Fergus R Vishwanathan S Garnett R , editors. NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates; 2017:6000-6010. ISBN: 9781510860964
  5. Ansar W Goswami S Chakrabarti A . A survey of transformer-based language models with focus on efficiency. arXiv. Preprint posted online on May 15, 2024. [doi: 10.48550/arXiv.2406.16893] · DOI
  6. Peng B Narayanan S Papadimitriou C . On limitations of the transformer architecture. arXiv. Preprint posted online on Feb 13, 2024. [doi: 10.48550/arXiv.2402.08164] · DOI
  7. Rahali, End-to-end transformer-based models in textual-based NLP, AI, 4, 54, 10.3390/ai4010004 · DOI
  8. Steinbrenner, Explaining anxiety prediction in psychotherapy transcripts: the role of patient linguistic features and theoretical constructs, Behav Res Ther, 194, 10.1016/j.brat.2025.104857 · DOI
  9. Liu B . Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press; 2020. [doi: 10.1017/9781108639286] · DOI
  10. Tausczik, The psychological meaning of words: LIWC and computerized text analysis methods, J Lang Soc Psychol, 29, 24, 10.1177/0261927X09351676 · DOI · Paper page
  11. Mohammad, Crowdsourcing a word–emotion association lexicon, Comput Intell, 29, 436, 10.1111/j.1467-8640.2012.00460.x · DOI
  12. Mohammad SM Turney P . NRC word-emotion association lexicon (version 2022, multilingual edition). NRC Emotion Lexicon. 2022. URL: https://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm [Accessed 29-10-2026]
  13. Pozzi FA Fersini E Messina E Liu B . Challenges of sentiment analysis in social networks: an overview. In: Sentiment Analysis in Social Networks. Elsevier; 2016:1-11. [doi: 10.1016/B978-0-12-804412-4.00001-2] · DOI
  14. Devlin J Chang MW Lee K Toutanova K . BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics; 2019:4171-4186. [doi: 10.18653/v1/N19-1423] · DOI · Paper page
  15. García-Vega M Díaz-Galiano MC García-Cumbreras MA et al. Overview of TASS 2020: introducing emotion detection. In: Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020). CEUR Workshop Proceedings; 2020:163-170. URL: https://investigacion.ujaen.es/documentos/5f9fc51a2999520478089dd1?lang=en [Accessed 29-10-2026]
  16. Pérez JM Furman DA Alonso Alemany L Luque FM . RoBERTuito: a pre-trained language model for social media text in Spanish. In: Proceedings of the Thirteenth International Conference on Language Resources and Evaluation (LREC 2022). European Language Resources Association; 2022:7235-7243. [doi: 10.63317/4v9eufgmyo6q] · DOI
  17. Pérez JM Rajngewerc M Giudici JC et al. pysentimiento: a Python toolkit for opinion mining and social NLP tasks. arXiv. Preprint posted online on Jun 17, 2021. [doi: 10.48550/arXiv.2106.09462] · DOI
  18. De Filippi E Nandi A Vicens J et al. Seeing through lies: correlation between physiological and sentiment metrics in the consumption and dissemination of fake news. SocArXiv. Preprint posted online on Oct 28, 2024. [doi: 10.31235/osf.io/9hwut] · DOI
  19. Espinola Gonzales, Análisis de sentimientos y emociones sobre gastronomía peruana usando minería de texto con Python [Sentiment and emotion analysis about Peruvian gastronomy using text mining with Python] [Article in Spanish], Rev Investig Hatun Yachay Wasi, 3, 126, 10.57107/hyw.v3i1.63 · DOI
  20. Taruc, Int J Intell Syst Appl Eng, 12, 1553
  21. Gross, Mental illness and well-being: an affect regulation perspective, World Psychiatry, 18, 130, 10.1002/wps.20618 · DOI
  22. Bar-Kalifa, The role of affect dynamics as mechanisms of change in mental health interventions: integrating applied and basic science, J Consult Clin Psychol, 92, 445, 10.1037/ccp0000905 · DOI
  23. Liekmeier, Affective behavior in parent couples undergoing couple therapy: contrasting case studies, Front Psychol, 12, 10.3389/fpsyg.2021.634276 · DOI
  24. Montesano, Reconnecting with relational values in highly conflicted separating couples: a narrative therapy‐informed relational interviewing teamwork‐based supervision approach, J Clin Psychol, 79, 1521, 10.1002/jclp.23480 · DOI
  25. Selcuk, My partner really gets me: affective reactivity to partner stress predicts greater relationship quality in new couples, J Pers Soc Psychol, 126, 895, 10.1037/pspp0000509 · DOI
  26. Sanri, The couple flourishing measure, Fam Process, 60, 457, 10.1111/famp.12632 · DOI
  27. Snyder DK Balderrama-Durbin C . Couple assessment. In: Lebow JL Snyder DK , editors. Clinical Handbook of Couple Therapy. 6th ed. Guilford Press; 2022:22-51. ISBN: 9781462550128
  28. Youn, Implementation science and practice-oriented research: convergence and complementarity, Adm Policy Ment Health, 51, 336, 10.1007/s10488-023-01296-5 · DOI
  29. Eberhardt, Decoding emotions: exploring the validity of sentiment analysis in psychotherapy, Psychother Res, 35, 174, 10.1080/10503307.2024.2322522 · DOI
  30. Eberhardt, Development and validation of large language model rating scales for automatically transcribed psychological therapy sessions, Sci Rep, 15, 10.1038/s41598-025-14923-y · DOI
  31. Xu, Emotion dysregulation and couple relationship satisfaction of clinical couples: an actor-partner interdependence model, Fam Process, 62, 1555, 10.1111/famp.12828 · DOI
  32. Boostani-Kashani, اثربخشی الگوی روایت درمانی بر مهارت های ارتباطی، ابرازگری هیجانی و همدلی در دختران وپسران آماده ازدواج [The effectiveness of narrative therapy model on communication skills, emotional expression, and empathy in girls and boys ready for marriage] [Article in Persian], Q J Soc Health, 8, 232, 10.22037/ch.v8i2.28959 · DOI
  33. Hogue, Online training of community therapists in observational coding of family therapy techniques: reliability and accuracy, Adm Policy Ment Health, 49, 139, 10.1007/s10488-021-01152-4 · DOI
  34. Tanana, How do you feel? Using natural language processing to automatically rate emotion in psychotherapy, Behav Res Methods, 53, 2069, 10.3758/s13428-020-01531-z · DOI
  35. Kinsey AC Pomeroy WB Martin CE . Sexual Behavior in the Human Male. W.B. Saunders Company; 1949. URL: https://archive.org/details/in.ernet.dli.2015.187552/page/n1/mode/2up [Accessed 30-10-2026]
  36. Saavedra‐Roa, Adaptation and validation of the couple flourishing measure among Spaniards, Fam Process, 65, 10.1111/famp.70204 · DOI
  37. Saavedra-Roa, Criterion-related validity of the couple flourishing measure in a Spanish population: distinguishing outpatients from community participants, Fam Process, 65, 10.1111/famp.70211 · DOI

Metadata

Authors
Saavedra-Roa, A., Pineda-Marin, C., Vallejo-Medina, P., & Montesano, A.
Venue
JMIR AI
Publisher
JMIR Publications Inc.
Published
2026-11-09
Volume
5
Pages
e94097-e94097
ISSN
2817-1705
DOI
10.2196/94097
Cited by
0
Language
en

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Saavedra-Roa, A., Pineda-Marin, C., Vallejo-Medina, P., & Montesano, A. (2026). Detecting Emotional Signals of Relationship Well-Being Using AI: A Cross-Sectional Comparison of Transformer-Based and Lexicon-Based Models for Couples Therapy Research. JMIR AI. https://doi.org/10.2196/94097

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