We present an annotated corpus of patient interview transcriptions, labeled for emotionality, polarity, intensity, and emotional category (at the sentence level), and for expression mode (at the token level). Three modes of expression are distinguished: Designated (explicit), Suggested (implicit causes), and Manifested (implicit consequences). The corpus has been collected during the GREMO-LING project and is used to measure the linguistic expressions of emotions in patients’ narratives. The corpus, consisting of 7,471 sentences, was used to fine-tune and evaluate several transformer-based language models, including the French BERT family. Sentence classification was performed for emotionality, emotion categories and expression modes. The best-performing models achieved F1 scores of 0.87 (emotionality, fine-tuned DistilCamemBERT), 0.58 (emotion categories, CamemBERTaV2), and 0.70 (expression modes, CamemBERT). We obtain solid results despite the high complexity of non-standard, spoken-derived data. These findings confirm the feasibility and relevance of automatic emotion detection in clinical discourse. We provide publicly available guidelines, annotated corpora and models, thereby establishing a methodological foundation for future research on the linguistic assessment of emotional regulation and its clinical implications, such as the evaluation of the Dialectical Behavioral Theray (DBT) in enhancing patients’ emotion regulation skills.

In this article, we aim to measure the patients’ progress in recognizing and naming emotions by capturing a variety of phenomena that express emotion in discourse. To do so, we introduce an emotion annotation scheme adapted for Acquired Brain Injury (ABI) patients’ narratives. We draw on recent research outcomes in line with linguistic and psychological theories of emotion in the development of French resources for Natural Language Processing (NLP). From this perspective and following Battistelli et al. (2022) guidelines, our protocol considers several means of expressing emotions, including prototypical expressions as well as implicit means. Its originality lies on the methodology adopted for its creation, as we combined, adapted, and tested several previous annotation schemes to create a tool tailored to our spoken clinical French corpus and its unique characteristics and challenges.