Nous proposons d’exploiter les recherches en sciences cognitives sur les émotions et la communication pour améliorer les modèles de langue pour l’analyse des émotions. Tout d’abord, nous présentons les principales théories des émotions en psychologie et en sciences cognitives. Puis, nous présentons les principales méthodes d’annotation des émotions en traitement automatique des langues et leurs liens avec les théories psychologiques. Nous présentons aussi les deux principaux types d’analyses de la communication des émotions en pragmatique cognitive. Enfin, en s’appuyant sur les recherches en sciences cognitives présentées, nous proposons des pistes pour améliorer les modèles de langue pour l’analyse des émotions. Nous suggérons que ces recherches ouvrent la voie à la construction de nouveaux schémas d’annotation et d’un possible benchmark pour la compréhension émotionnelle, prenant en compte différentes facettes de l’émotion et de la communication chez l’humain.

We propose leveraging cognitive science research on emotions and communication to improve language models for emotion analysis. First, we present the main emotion theories in psychology and cognitive science. Then, we introduce the main methods of emotion annotation in natural language processing and their connections to psychological theories. We also present the two main types of analyses of emotional communication in cognitive pragmatics. Finally, based on the cognitive science research presented, we propose directions for improving language models for emotion analysis. We suggest that these research efforts pave the way for constructing new annotation schemes, methods, and a possible benchmark for emotional understanding, considering different facets of human emotion and communication.

The study of dreams has been central to understanding human (un)consciousness, cognition, and culture for centuries. Analyzing dreams quantitatively depends on labor-intensive, manual annotation of dream narratives. We automate this process through a natural language sequence-to-sequence generation framework. This paper presents the first study on character and emotion detection in the English portion of the open DreamBank corpus of dream narratives. Our results show that language models can effectively address this complex task. To get insight into prediction performance, we evaluate the impact of model size, prediction order of characters, and the consideration of proper names and character traits. We compare our approach with a large language model using in-context learning. Our supervised models perform better while having 28 times fewer parameters. Our model and its generated annotations are made publicly available.

Emotion regulation is a crucial element in dealing with emotional events and has positive effects on mental health. This paper aims to provide a more comprehensive understanding of emotional events by introducing a new French corpus of emotional narratives collected using a questionnaire for emotion regulation. We follow the theoretical framework of the Component Process Model which considers emotions as dynamic processes composed of four interrelated components (behavior, feeling, thinking and territory). Each narrative is related to a discrete emotion and is structured based on all emotion components by the writers. We study the interaction of components and their impact on emotion classification with machine learning methods and pre-trained language models. Our results show that each component improves prediction performance, and that the best results are achieved by jointly considering all components. Our results also show the effectiveness of pre-trained language models in predicting discrete emotion from certain components, which reveal differences in how emotion components are expressed.