A total of 112 adults residing in Spain participated in this exploratory cross-sectional study. Participants were recruited through nonprobability convenience sampling, given the exploratory and methodological focus of the research. All participants had been in a romantic relationship for at least 6 months at the time of the study. No a priori sample size calculation was performed due to the exploratory nature of the study.

All participants completed an online survey administered via Qualtrics, which included sociodemographic and psychosexual information, the CFM, and an open-ended question regarding their perceptions of their affective-sexual relationship. The sampling was conducted between June 2023 and June 2024. Recruitment was conducted primarily through dissemination on social media platforms. Participants accessed the study via a link, provided digital informed consent, and completed the survey anonymously. The average response time of the battery was 28.08 minutes.

Correlations between the total scores on the CFM and the emotional and sentiment indices generated by both NLP models were examined using Pearson correlation coefficients (r). For each participant, the scores generated by both models were matched with their corresponding CFM total score, which served as a common external criterion for evaluating the associations between the affective indices and relationship flourishing. To determine whether the correlations obtained using Pysentimiento differed significantly from those obtained using the NRC Emotion Lexicon, Steiger Z tests for dependent correlations were performed for the emotion and sentiment categories shared by both models. This test was selected because the correlations were derived from the same sample and shared the CFM score as a common variable, while accounting for the correlation between the corresponding model-derived indices. Categories available in only 1 model were not included in these comparisons. The 95% CIs were estimated for the reported effects. Additionally, an exploratory ordinary least squares linear regression was performed to examine the trend in CFM total scores across dominant sentiment categories. Dominant sentiment was defined as the category with the highest predicted probability from Pysentimiento, with categories coded as ordered levels (1=negative, 2=neutral, and 3=positive). Missing data were handled using available-case analysis. All analyses were carried out using R [39] in the R Studio environment (version 2023.06.1+524; Posit PBC) [40].

This study was approved by the Ethics Committee, which operates independently of the Faculty of Psychology and Educational Sciences. This study was conducted in accordance with the principles of the 1975 Declaration of Helsinki, as revised in 1983 by the Research Ethics Committee. All participants voluntarily agreed to participate in the study, and the ethical committee evaluated the procedure to obtain consent. This study was approved by the Ethics Committee of the university, with an approval number CE23-PF02.

The sample comprised 112 adults residing in Spain. Age data were available for 102 participants, with a mean age of 34.66 (SD 10.22) years. Of the 112 participants, 24.1% (n=27; mean age 34.0, SD 9.37 years) were men and 75.9% (n=85; mean age 34.85, SD 10.49 years) were cisgender women. No significant age differences were observed between genders (t₁₀₀=−0.34; P=.73). Regarding sexual orientation, 89.3% (n=100) identified as exclusively or predominantly heterosexual, 5.4% (n=6) as exclusively or predominantly homosexual, 2.7% (n=3) reported intermediate patterns of heterosexual and homosexual attraction or contact, and 1.8% (n=2) identified as asexual. Sexual orientation data were missing for 1 (0.9%) participant.

Correlations between scores on the NRC Emotion Lexicon and the CFM were examined. The emotions of anger, disgust, fear, sadness, and the negative category showed significant negative correlations with CFM scores, although their magnitudes were low (r=–0.19 to r=–0.27). The dimensions of joy, surprise, trust, and the positive category did not show significant associations.

Subsequently, the correlations between the scores generated by Pysentimiento and the CFM were analyzed. Compared with the NRC Emotion Lexicon, this transformer model showed stronger and more consistent associations with CFM scores. The emotions of disgust, sadness, fear, and anger showed higher negative correlations (r=–0.27 to r=–0.49). Joy, as well as the positive and negative categories, showed significant and moderate correlations, highlighting the strong association between negative (r=–0.74) and positive feelings (r=0.66) with the CFM.

Statistically significant differences between the models were observed for disgust, joy, sadness, surprise, and positive and negative sentiment. Pysentimiento-derived scores showed stronger associations with CFM scores for disgust, joy, sadness, and both sentiment dimensions, whereas the associations for anger and fear did not differ significantly between models. For surprise, the models showed correlations in opposite directions, with a nonsignificant positive association for NRC Emotion Lexicon and a significant negative association for Pysentimiento. Overall, these findings indicate that the transformer-based approach showed stronger criterion-related associations across several affective dimensions, although this pattern was not uniform across all categories (Table 1).

As an additional exploratory analysis, we examined the overall relationship between the dominant sentiment identified by Pysentimiento (ie, negative, neutral, or positive) and CFM scores. A significant positive linear trend was observed (B=22.80, 95% CI 18.52-27.09; t₁₁₀=10.55; P<.001), explaining 50.3% of the variance in CFM scores (R²=0.503). Mean CFM scores increased from 40.1 (SD 20.6; n=13) in the negative sentiment group to 75.7 (SD 18.4; n=19) in the neutral group and 90.2 (SD 13.6; n=80) in the positive group (Figure 1).

Given that Pysentimiento outperformed the lexical approach, narrative examples were included to illustrate how the model infers emotional probabilities from real descriptions of romantic relationships (Table 2). These examples show greater sensitivity to affective nuances, contextual recognition of emotional tone, and consistency between discursive meanings and assigned probabilities.

The purpose of this study was to conduct a methodological comparison between 2 approaches to NLP: a lexicon-based model (NRC Emotion Lexicon) and a transformer-based model (Pysentimiento). Specifically, we examined the affective indices generated by each approach from spontaneous narratives about affective-sexual relationships and how these outputs relate to couple flourishing, as measured by the CFM.

The results show systematic differences between the 2 approaches. The NRC lexical model identified small but significant negative associations between CFM scores and anger, disgust, fear, sadness, and negative sentiment. These results are consistent with previous literature indicating that dictionary-based approaches can detect general affective trends but have significant limitations in capturing relational nuances, contextual ambiguity, polysemy, or implicit affective expressions [9,10,13]. This coincides with previous observations that point to the inherent limitations of lexicon-based models in emotionally complex and intimate domains [11,12].

In contrast, the transformer model (Pysentimiento) showed stronger associations with CFM scores across several affective dimensions. These associations suggest that Pysentimiento-derived indices may better reflect aspects of relational well-being captured by the CFM. Although this pattern is consistent with the potential advantages of contextual language representations, the present study did not directly assess the models’ ability to interpret linguistic nuance. These findings are consistent with recent studies that highlight the ability of deep learning–based models to represent complex affective patterns in natural languages [4,6,14]. This suggests that transformer-based approaches may offer methodological advantages when examining affective patterns in relational narratives.

From a theoretical and methodological perspective, these results are particularly relevant for the study of sexual-affective bonds. Narratives of this type of bond are often laden with metaphors, implicit meanings, relational nuances, and affective constructions that can be challenging for lexicon-based approaches to interpret. The stronger criterion-related associations observed for Pysentimiento suggest that transformer-based approaches may provide useful indicators of affective patterns associated with couple flourishing.

The observed linear trend, whereby CFM scores increased across the ordered dominant sentiment categories, provides additional evidence of an association between Pysentimiento-derived sentiment classifications and couple flourishing [26]. These findings contribute to the growing body of research on criterion-related evaluation of computational emotion detection in relational research.

Taken together, these findings highlight the potential of integrating NLP tools into research on sexual-affective relationships and, following further validation, into clinical applications. Transformer models may complement traditional self-report measures and offer alternative ways to study affective processes using natural discourse data, thus expanding theoretical and methodological possibilities in this field [27,28]. The comparison suggests that different NLP approaches may vary in their suitability for relational contexts, with contextual language processing representing a potentially relevant methodological advantage when examining affective meanings in intimate narratives.

In this context, these findings are particularly relevant to future research on clinical processes in couples therapy. Automated discourse analysis may provide additional information about affective signals that emerge spontaneously in interactions, which have traditionally been difficult to capture through manual coding or retrospective self-report measures [27]. Transformer-based models could be investigated for their potential to identify patterns of emotional communication associated with relational satisfaction or distress; detect moment-to-moment affective shifts during the process (ie, process-level markers); evaluate mechanisms of change, such as dyadic regulation, emotional responsiveness, or the emergence of shared narratives; and monitor therapeutic progress over time, potentially complementing traditional clinical assessments with context-sensitive indicators. However, these applications require direct empirical validation in therapeutic settings. These possibilities are consistent with recent advancements in psychotherapy-oriented NLP, in which sentiment-analysis models have been used to examine clients’ emotional expression across sessions [29] and to predict clinically relevant variables such as symptom severity [30]. Such potential applications are also consistent with findings by Steinbrenner et al [8], who demonstrated that NLP applied to psychotherapy transcripts could predict anxiety severity, suggesting possible applications for monitoring in therapeutic contexts. These contributions coincide with recent proposals suggesting that NLP innovations can deepen the understanding of therapeutic processes and open new avenues for analyzing relational change [34].

This study had several limitations. First, the sample consisted exclusively of participants residing in Spain, which limits the generalizability of the findings to other linguistic and cultural contexts. Second, although the transformer model showed stronger associations with CFM scores across several affective dimensions, these associations varied across emotion categories, indicating that the observed advantages were not uniform. This variability is consistent with challenges in computational emotion analysis previously discussed in the NLP literature [6]. Third, the cross-sectional design prevents conclusions about causal relationships between emotional discourse patterns and relational well-being. Fourth, the comparison was limited to 1 lexicon-based approach and 1 transformer-based model, restricting the generalizability of the findings to other tools within these computational paradigms. Fifth, multiple statistical comparisons were conducted without adjustment for multiple testing, increasing the possibility of type I errors. Therefore, individual statistically significant findings should be interpreted cautiously and require replication in independent samples. Finally, the analysis relied on a single open-ended narrative per participant and used CFM scores as an external criterion rather than independently annotated emotion labels. Consequently, the findings provide evidence of criterion-related associations but do not establish the accuracy of emotion classification. Furthermore, the use of nonprobability convenience sampling and self-reported data may have introduced selection and response biases, which should be considered when interpreting the findings.

Future research should extend the comparison to other languages, populations, and various types of sexual-affective relationships, as well as explore the integration of transformer models with qualitative methodologies or longitudinal designs to examine the evolution of emotional discourse in couples over time and assess the temporal reliability of model-derived indices. It would also be relevant to evaluate the use of these tools in clinical or intervention contexts, carefully considering ethical aspects related to privacy and the analysis of sensitive narratives. Furthermore, future research should evaluate how these models can be integrated into the observational analysis of therapeutic sessions to study the interactional microprocesses and mechanisms of change throughout treatment.

This methodological comparison found that Pysentimiento showed stronger associations with couple flourishing than the NRC Emotion Lexicon across several affective dimensions, although these differences were not consistent across all categories. These results contribute to methodological advances in the study of romantic and sexual relationships by highlighting the value of theory-driven external criteria for evaluating computational emotion detection and open new possibilities for the automated analysis of emotional discourse in relational contexts. Furthermore, they suggest promising directions for research on processes in couples therapy, highlighting the potential of NLP as a complementary tool in clinical research on relational processes, subject to further empirical validation.