Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation (2023)

Abstract: 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.

## introduction emotion analysis in text consists of associating an emotion from a predefined set (e.g. fear, joy, sadness) to a textual unit (e.g. word, clause, sentence). several psychological theories are used to define the emotion classes to be predicted. basic emotion theories @xcite consider discrete emotions shared by all, as they may have innate neural substrates and universal behavioral phenotypes. dimensional theories @xcite define emotions through affective dimensions, such as the degree of agreeableness (valence) and the degree of physiological activation @xcite . previous studies @xcite ) have conducted analyses on various corpora for emotion classification in text. most of them neglect the existing psychological knowledge about emotions, which can be used to clarify what an emotion is and how it can be caused. to the best of our knowledge, only a few approaches incorporate cognitive psychological theories to classify emotions in texts. these include a knowledgebase-oriented modeling of emotional events @xcite , a corpus annotated according to dimensions of cognitive appraisal of events @xcite , an annotation scheme for emotions inspired by psycholinguistics @xcite , and the identification of emotion component classes @xcite according to the component process model (cpm) @xcite in cognitive psychology. these papers, like ours, are based on the cognitive appraisal theory @xcite , which posits that emotions arise from the evaluation of an event based on various cognitive criteria, such as relevance, implication, coping, and normative significance. the cpm is rooted in this theory and defines emotion as a set of cognitive appraisals that modulate the expression of five components in reaction to an event (cognitive appraisal, physiological response, motor expression, action tendency, and subjective feeling). our chosen components are closely related to the components originally proposed in the cpm. in this paper, we follow the theoretical framework of the cpm by considering emotions as dynamic processes composed of four interrelated components: behavior ("i'm giving a lecture"), feeling ("my heart is beating fast"), thinking ("i think he's disrupting my lecture") and territory ("he attacks my ability to be respected") proposed by @xcite . in our corpus, each narrative is structured by the writers according to these components. component answer behavior i'm giving a lecture on a friday morning at 8:30. a student goes out and comes back a few moments later with a coffee in his hand. we rely on the same assumptions made by @xcite , namely that emotions in a text are expressed in several ways. emotion components are associated with different linguistic realizations. in this paper, we study how emotions are expressed through components by introducing a new french corpus composed of emotional narratives. narratives were collected with a questionnaire following a new psychological method, called cognitive analysis of @xcite , which aims to modify (negative) representations of an emotional event to help people better regulate their emotions. our corpus is structured according to emotion components and contains 812 narratives, corresponding to 3082 answers. each narrative contains several answers, and each answer corresponds to a single component. in this paper, we describe the annotation of our corpus and evaluate traditional machine learning methods and pre-trained language models for discrete emotion classification based on emotion components. to the best of our knowledge, this work is the first to study the interaction between linguistic realizations of components for emotion classification. we aim to answer several questions: does a component influence emotion prediction and, if so, does it increase or decrease performance? does each component contribute equally or unequally to the prediction? does considering all components lead to the best performance? contributions we present a new french corpus composed of emotional narratives structured according to four 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, allowing us to study the interaction of components and their impact on emotion classification. we evaluate the influence of components on emotion classification using traditional machine learning methods and pre-trained language models (camembert). 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 that camembert effectively predict discrete emotion from thinking, but do not improve performance from feeling compared to traditional machine learning approaches, which reveal differences in how emotion components are expressed. we believe that our analysis can provide a further insight into the semantic core of emotion expressions in text. appraisal theories the cognitive appraisal theory @xcite identifies cognitive dimensions of emotion, considered criteria for evaluating an event. for example, it considers that an individual evaluates how an event helps him or her in satisfying a need or accomplishing a goal. there are other appraisal criteria, such as the ability to cope with an event based on resources available to the individual. the type and intensity of an emotion provoked by an event depend on the result of cognitive appraisals. component process model cognitive appraisals are integrated in the component process model (cpm) @xcite . it considers emotion as the expression of several components (cognitive appraisal, physiological response, motor expression, action tendency, and subjective feeling) that synchronize in reaction to an event. the cognitive appraisals of an event modulate the expression of components. for example, during an exam, i evaluate my ability to solve an exercise; i think i do not have the skills to solve it and will get a bad mark (cognitive appraisal). i panic (subjective feeling), i sweat (physiological response), my legs shake (motor expression), i feel like getting up and running away from the classroom (action tendency). in this text, we can infer that i am afraid (fear). our corpus explores the interaction between linguistic realizations of components. despite being closely related, our components proposed by the cognitive analysis of emotion differ from the original ones presented by the cpm. ## conclusion and future work emotion regulation is a critical aspect of emotional events and has noteworthy implications for psychological well-being. in this paper, we aimed to provide a more comprehensive understanding of emotional events by introducing a french corpus of 812 emotional narratives (3082 answers). our corpus was annotated following the component process model and was collected using a recent psychological method for emotion regulation, named the cognitive analysis of emotion. @xcite were the first to annotate corpora with external annotators according to emotion components. our corpus differs because each narrative is annotated by the writers and is structured according to all components (behavior, feeling, think-ing, and territory), which allows for the study of their interaction. we employed traditional machine learning methods and pre-trained language models (camem-bert) to investigate the interaction of components for discrete emotion classification. our results show that each component is useful for classifying discrete emotions, and that the model with the best performance considers all components, supporting scherer's hypothesis @xcite that components synchronize during an emotional event. our results also show that camembert effectively predict discrete emotion from thinking, but do not improve performance from feeling compared to traditional machine learning approaches, which reveal differences in how emotion components are expressed. we hypothesize that this may be explained by emotion expression modes studied in linguistics @xcite . to test this hypothesis, we plan to annotate emotion expression modes in our corpus using a recent annotation scheme proposed by @xcite .