"A tour de force. If you read this book, you'll never look at other people in quite the same way again." Malcolm GladwellRenowned psychologist Paul Ekman explains the roots of our emotions anger, fear, disgust, sadness, and happiness and shows how they cascade across our faces, providing clear signals to those who can identify the clues. As featured in Malcolm Gladwell's bestseller "Blink," Ekman's Facial Action Coding System offers intense training in recognizing feelings in spouses, children, colleagues, even strangers on the street. In "Emotions Revealed," Ekman distills decades of research into a practical, mind-opening, and life-changing guide to reading the emotions of those around us. He answers such questions as: How does our body signal to others whether we are slightly sad or anguished, peeved or enraged? Can we learn to distinguish between a polite smile and the genuine thing? Can we ever truly control our emotions? Packed with unique exercises and photographs, and a new chapter on emotions and lying that encompasses security and terrorism as well as gut decisions, "Emotions Revealed" is an indispensable resource for navigating our emotional world."

Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life

Speaker emotion recognition is achieved through processing methods that include isolation of the speech signal and extraction of selected features for the final classification. In terms of acoustics, speech processing techniques offer extremely valuable paralinguistic information derived mainly from prosodic and spectral features. In some cases, the process is assisted by speech recognition systems, which contribute to the classification using linguistic information. Both frameworks deal with a very challenging problem, as emotional states do not have clear-cut boundaries and often differ from person to person. In this article, research papers that investigate emotion recognition from audio channels are surveyed and classified, based mostly on extracted and selected features and their classification methodology. Important topics from different classification techniques, such as databases available for experimentation, appropriate feature extraction and selection methods, classifiers and performance issues are discussed, with emphasis on research published in the last decade. This survey also provides a discussion on open trends, along with directions for future research on this topic.

Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011

Emotions research is now routinely grounded in evolution, but explicit evolutionary analyses of emotions remain rare. This article considers the implications of natural selection for several classic questions about emotions and emotional disorders. Emotions are special modes of operation shaped by natural selection. They adjust multiple response parameters in ways that have increased fitness in adaptively challenging situations that recurred over the course of evolution. They are valenced because selection shapes special processes for situations that have influenced fitness in the past. In situations that decrease fitness, negative emotions are useful and positive emotions are harmful. Selection has partially differentiated subtypes of emotions from generic precursor states to deal with specialized situations. This has resulted in untidy emotions that blur into each other on dozens of dimensions, rendering the quest for simple categorically distinct emotions futile. Selection has shaped flexible mechanisms that control the expression of emotions on the basis of an individual's appraisal of the meaning of events for his or her ability to reach personal goals. The prevalence of emotional disorders can be attributed to several evolutionary factors.

/pdf/evolution-emotions-and-emotional-disorders-532djz8ud2.pdf

Evolution, emotions, and emotional disorders.

In recent years, increasing impact of social networks on people's opinions and decision making has attracted lots of attention. Microblogging, one of the most popular social network applications that allows people to share ideas and discuss over various topics, is taken as a rich resource of opinion and emotion data. In this paper, we propose and implement a novel method for identifying emotions in microblog posts. Unlike traditional approaches which are mostly based on statistical methods, we try to infer and extract the reasons of emotions by importing knowledge and theories from other fields such as Sociology. Based on the theory that a triggering cause event is an integral part of emotion, the technique of emotion cause extraction is used as a crucial step to improve the quality of selected features. First, after thorough analysis on sample data we constructed an automatic rule-based system to detect and extract the cause event of each emotional post. We build an emotion corpus with Chinese microblog posts labeled by human annotators. Then a classifier is trained to classify emotions in microblog posts based on extracted cause events. The overall performance of our system is very promising. The experiment results show that our approach is effective in selecting informative features. Our system outperformed the baseline noticeably in most cases, suggesting its great potential. This exploration should provide a new way to look at the emotion classification task and lay the ground for future research on textual emotion processing.

Anna Wierzbicka

Papers

Emotions Across Languages and Cultures: Diversity and Universals: Emotional universals