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Doing Emotions History
How do emotions change over time? When is hate honorable? What happens when love is translated into different languages? Such questions are now being addressed by historians who trace how emotions have been expressed and understood in different cultures throughout history. Doing Emotions History explores the history of feelings such as love, joy, grief, nostalgia as well as a wide range of others, bringing together the latest and most innovative scholarship on the history of the emotions. Spanning the globe from Asia and Europe to North America, the book provides a crucial overview of this emerging discipline. An international group of scholars reviews the field's current status and variations, addresses many of its central debates, provides models and methods, and proposes an array of possibilities for future research. Emphasizing the field's intersections with anthropology, psychology, sociology, neuroscience, data-mining, and popular culture, this groundbreaking volume demonstrates the affecting potential of doing emotions history.
eBook
Emotion-Regulation Choice
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.
Journal Article
A survey of state-of-the-art approaches for emotion recognition in text
Emotion recognition in text is an important natural language processing (NLP) task whose solution can benefit several applications in different fields, including data mining, e-learning, information filtering systems, human–computer interaction, and psychology. Explicit emotion recognition in text is the most addressed problem in the literature. The solution to this problem is mainly based on identifying keywords. Implicit emotion recognition is the most challenging problem to solve because such emotion is typically hidden within the text, and thus, its solution requires an understanding of the context. There are four main approaches for implicit emotion recognition in text: rule-based approaches, classical learning-based approaches, deep learning approaches, and hybrid approaches. In this paper, we critically survey the state-of-the-art research for explicit and implicit emotion recognition in text. We present the different approaches found in the literature, detail their main features, discuss their advantages and limitations, and compare them within tables. This study shows that hybrid approaches and learning-based approaches that utilize traditional text representation with distributed word representation outperform the other approaches on benchmark corpora. This paper also identifies the sets of features that lead to the best-performing approaches; highlights the impacts of simple NLP tasks, such as part-of-speech tagging and parsing, on the performances of these approaches; and indicates some open problems.
Journal Article
EEG-Based BCI Emotion Recognition: A Survey
Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user’s emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.
Journal Article
The brain basis of emotion: A meta-analytic review
Researchers have wondered how the brain creates emotions since the early days of psychological science. With a surge of studies in affective neuroscience in recent decades, scientists are poised to answer this question. In this target article, we present a meta-analytic summary of the neuroimaging literature on human emotion. We compare the locationist approach (i.e., the hypothesis that discrete emotion categories consistently and specifically correspond to distinct brain regions) with the psychological constructionist approach (i.e., the hypothesis that discrete emotion categories are constructed of more general brain networks not specific to those categories) to better understand the brain basis of emotion. We review both locationist and psychological constructionist hypotheses of brain–emotion correspondence and report meta-analytic findings bearing on these hypotheses. Overall, we found little evidence that discrete emotion categories can be consistently and specifically localized to distinct brain regions. Instead, we found evidence that is consistent with a psychological constructionist approach to the mind: A set of interacting brain regions commonly involved in basic psychological operations of both an emotional and non-emotional nature are active during emotion experience and perception across a range of discrete emotion categories.
Journal Article