Résultats de recherche pour « Emotion regulation »

Recent advances in machine learning have led to computer systems that are human-like in behaviour. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behaviour. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis.

Despite centuries of speculation about how to manage negative emotions, little is actually known about which emotion-regulation strategies people choose to use when confronted with negative situations of varying intensity. On the basis of a new process conception of emotion regulation, we hypothesized that in low-intensity negative situations, people would show a relative preference to choose to regulate emotions by engagement reappraisal, which allows emotional processing. However, we expected people in high-intensity negative situations to show a relative preference to choose to regulate emotions by disengagement distraction, which blocks emotional processing at an early stage before it gathers force. In three experiments, we created emotional contexts that varied in intensity, using either emotional pictures (Experiments 1 and 2) or unpredictable electric stimulation (Experiment 3). In response to these emotional contexts, participants chose between using either reappraisal or distraction as an emotion-regulation strategy. Results in all experiments supported our hypothesis. This pattern in the choice of emotion-regulation strategies has important implications for the understanding of healthy adaptation.

Textual sentiment analysis and emotion detection consists in retrieving the sentiment or emotion carried by a text or document. This task can be useful in many domains: opinion mining, prediction, feedbacks, etc. However, building a general purpose tool for doing sentiment analysis and emotion detection raises a number of issues, theoretical issues like the dependence to the domain or to the language but also pratical issues like the emotion representation for interoperability. In this paper we present our sentiment/emotion analysis tools, the way we propose to circumvent the di culties and the applications they are used for.

Emotion regulation (ER) refers to attempts to influence emotions in ourselves or others. Over the past several decades, ER has become a popular topic across many subdisciplines within psychology. One framework that has helped to organize work on ER is the process model of ER, which distinguishes 5 families of strategies defined by when they impact the emotion generation process. The process model embeds these ER strategies in stages in which a need for regulation is identified, a strategy is selected and implemented, and monitoring occurs to track success. Much of the research to date has focused on a strategy called cognitive reappraisal, which involves changing how one thinks about a situation to influence one's emotional response. Reappraisal is thought to be generally effective and adaptive, but there are important qualifications. In this article, we use reappraisal as an example to illustrate how we might consider 4 interrelated issues: (a) the consequences of using ER, either when instructed or spontaneous; (b) how ER success and frequency are shaped by individual and environmental determinants; (c) the psychological and neurobiological mechanisms that make ER possible; and (d) interventions that might improve how well and how often people use ER. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Most approaches to emotion analysis of social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik. However, art (such as literature) enables engagement in a broader range of more complex and subtle emotions. These have been shown to also include mixed emotional responses. We consider emotions in poetry as they are elicited in the reader, rather than what is expressed in the text or intended by the author. Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within their context. We evaluate this novel setting in an annotation experiment both with carefully trained experts and via crowdsourcing. Our annotation with experts leads to an acceptable agreement of kappa = .70, resulting in a consistent dataset for future large scale analysis. Finally, we conduct first emotion classification experiments based on BERT, showing that identifying aesthetic emotions is challenging in our data, with up to .52 F1-micro on the German subset. Data and resources are available at https://github.com/tnhaider/poetry-emotion

Emotion estimation in images is a challenging task, typically using computer vision methods to directly estimate people's emotions using face, body pose and contextual cues. In this paper, we explore whether Large Language Models (LLMs) can support the contextual emotion estimation task, by first captioning images, then using an LLM for inference. First, we must understand: how well do LLMs perceive human emotions? And which parts of the information enable them to determine emotions? One initial challenge is to construct a caption that describes a person within a scene with information relevant for emotion perception. Towards this goal, we propose a set of natural language descriptors for faces, bodies, interactions, and environments. We use them to manually generate captions and emotion annotations for a subset of 331 images from the EMOTIC dataset. These captions offer an interpretable representation for emotion estimation, towards understanding how elements of a scene affect emotion perception in LLMs and beyond. Secondly, we test the capability of a large language model to infer an emotion from the resulting image captions. We find that GPT-3.5, specifically the text-davinci-003 model, provides surprisingly reasonable emotion predictions consistent with human annotations, but accuracy can depend on the emotion concept. Overall, the results suggest promise in the image captioning and LLM approach.

Recent developments in the study of cognitive emotion regulation illustrate how functional imaging is extending behavioral analyses. Imaging studies have contributed to the development of a multi-level model of emotion regulation that describes the interactions between neural systems implicated in emotion generation and those implicated in emotional control. In this article, we review imaging studies of one type of cognitive emotion regulation, namely reappraisal. We show how imaging studies have contributed to the construction of this model, illustrate the interplay of psychological theory and neuroscience data in its development, and describe how this model can be used as the basis for future basic and translational research.

Emotion regulation is the process of consciously altering one's affective state, that is the underlying emotional state such as happiness, confidence, guilt, anger etc. The ability to effectively regulate emotions is necessary for functioning efficiently in everyday life. Today, the pervasiveness of digital technology is being purposefully employed to modify our affective states, a process known as digital emotion regulation. Understanding digital emotion regulation can help support the rise of ethical technology design, development, and deployment. This article presents an overview of digital emotion regulation in social media applications, as well as a synthesis of recent research on emotion regulation interventions for social media. We share our findings from analysing state-of-the-art literature on how different social media applications are utilised at different stages in the process of emotion regulation.