Seq2emo: a Sequence to Multi-Label Emotion Classification Model
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The Influence of Emotional States on Short-Term Memory Retention by Using Electroencephalography (EEG) Measurements: a Case Study
The Influence of Emotional States on Short-term Memory Retention by using Electroencephalography (EEG) Measurements: A Case Study Ioana A. Badara1, Shobhitha Sarab2, Abhilash Medisetty2, Allen P. Cook1, Joyce Cook1 and Buket D. Barkana2 1School of Education, University of Bridgeport, 221 University Ave., Bridgeport, Connecticut, 06604, U.S.A. 2Department of Electrical Engineering, University of Bridgeport, 221 University Ave., Bridgeport, Connecticut, 06604, U.S.A. Keywords: Memory, Learning, Emotions, EEG, ERP, Neuroscience, Education. Abstract: This study explored how emotions can impact short-term memory retention, and thus the process of learning, by analyzing five mental tasks. EEG measurements were used to explore the effects of three emotional states (e.g., neutral, positive, and negative states) on memory retention. The ANT Neuro system with 625Hz sampling frequency was used for EEG recordings. A public-domain library with emotion-annotated images was used to evoke the three emotional states in study participants. EEG recordings were performed while each participant was asked to memorize a list of words and numbers, followed by exposure to images from the library corresponding to each of the three emotional states, and recall of the words and numbers from the list. The ASA software and EEGLab were utilized for the analysis of the data in five EEG bands, which were Alpha, Beta, Delta, Gamma, and Theta. The frequency of recalled event-related words and numbers after emotion arousal were found to be significantly different when compared to those following exposure to neutral emotions. The highest average energy for all tasks was observed in the Delta activity. Alpha, Beta, and Gamma activities were found to be slightly higher during the recall after positive emotion arousal. -
Anger in Negotiations: a Review of Causes, Effects, and Unanswered Questions David A
Negotiation and Conflict Management Research Anger in Negotiations: A Review of Causes, Effects, and Unanswered Questions David A. Hunsaker Department of Management, University of Utah, Salt Lake City, UT, U.S.A. Keywords Abstract anger, negotiation, emotion, attribution. This article reviews the literature on the emotion of anger in the negotia- tion context. I discuss the known antecedents of anger in negotiation, as Correspondence well as its positive and negative inter- and intrapersonal effects. I pay par- David Hunsaker, David Eccles ticular attention to the apparent disagreements within the literature con- School of Business, Spencer Fox cerning the benefits and drawbacks of using anger to gain advantage in Eccles Business Building, negotiations and employ Attribution Theory as a unifying mechanism to University of Utah, 1655 East Campus Center Drive, Salt Lake help explain these diverse findings. I call attention to the weaknesses evi- City, UT 84112, U.S.A.; e-mail: dent in current research questions and methodologies and end with sug- david.hunsaker@eccles. gestions for future research in this important area. utah.edu. What role does anger play in the negotiation context? Can this emotion be harnessed and manipulated to increase negotiating power and improve negotiation outcomes, or does it have inevitable downsides? The purpose of this article is to review the work of scholars that have focused on the emotion of anger within the negotiation context. I begin by offering some background on the field of research to be reviewed. I then explain why Attri- bution Theory is particularly helpful in making sense of diverse findings in the field. -
Emotion Detection in Twitter Posts: a Rule-Based Algorithm for Annotated Data Acquisition
2020 International Conference on Computational Science and Computational Intelligence (CSCI) Emotion detection in Twitter posts: a rule-based algorithm for annotated data acquisition Maria Krommyda Anastatios Rigos Institute of Communication and Computer Systems Institute of Communication and Computer Systems Athens, Greece Athens, Greece [email protected] [email protected] Kostas Bouklas Angelos Amditis Institute of Communication and Computer Systems Institute of Communication and Computer Systems Athens, Greece Athens, Greece [email protected] [email protected] Abstract—Social media analysis plays a key role to the person that provided the text and it is categorized as positive, understanding of the public’s opinion regarding recent events negative, or neutral. Such analysis is mainly used for movies, and decisions, to the design and management of advertising products and persons, when measuring their appeal to the campaigns as well as to the planning of next steps and mitigation actions for public relationship initiatives. Significant effort has public is of interest [4] and require extended quantities of text been dedicated recently to the development of data analysis collected over a long period of time from different sources to algorithms that will perform, in an automated way, sentiment ensure an unbiased and objective output. analysis over publicly available text. Most of the available While this technique is very popular and well established, research work, focuses on binary categorizing text as positive or with many important use cases and applications, there are negative without further investigating the emotions leading to that categorization. The current needs, however, for in-depth analysis other equally important use cases where such analysis fail to of the available content combined with the complexity and multi- provided the needed information. -
1 the Development of Empathy: How, When, and Why Nicole M. Mcdonald & Daniel S. Messinger University of Miami Department Of
1 The Development of Empathy: How, When, and Why Nicole M. McDonald & Daniel S. Messinger University of Miami Department of Psychology 5665 Ponce de Leon Dr. Coral Gables, FL 33146, USA 2 Empathy is a potential psychological motivator for helping others in distress. Empathy can be defined as the ability to feel or imagine another person’s emotional experience. The ability to empathize is an important part of social and emotional development, affecting an individual’s behavior toward others and the quality of social relationships. In this chapter, we begin by describing the development of empathy in children as they move toward becoming empathic adults. We then discuss biological and environmental processes that facilitate the development of empathy. Next, we discuss important social outcomes associated with empathic ability. Finally, we describe atypical empathy development, exploring the disorders of autism and psychopathy in an attempt to learn about the consequences of not having an intact ability to empathize. Development of Empathy in Children Early theorists suggested that young children were too egocentric or otherwise not cognitively able to experience empathy (Freud 1958; Piaget 1965). However, a multitude of studies have provided evidence that very young children are, in fact, capable of displaying a variety of rather sophisticated empathy related behaviors (Zahn-Waxler et al. 1979; Zahn-Waxler et al. 1992a; Zahn-Waxler et al. 1992b). Measuring constructs such as empathy in very young children does involve special challenges because of their limited verbal expressiveness. Nevertheless, young children also present a special opportunity to measure constructs such as empathy behaviorally, with less interference from concepts such as social desirability or skepticism. -
Machine Learning Based Emotion Classification Using the Covid-19 Real World Worry Dataset
Anatolian Journal of Computer Sciences Volume:6 No:1 2021 © Anatolian Science pp:24-31 ISSN:2548-1304 Machine Learning Based Emotion Classification Using The Covid-19 Real World Worry Dataset Hakan ÇAKAR1*, Abdulkadir ŞENGÜR2 *1Fırat Üniversitesi/Teknoloji Fak., Elektrik-Elektronik Müh. Bölümü, Elazığ, Türkiye ([email protected]) 2Fırat Üniversitesi/Teknoloji Fak., Elektrik-Elektronik Müh. Bölümü, Elazığ, Türkiye ([email protected]) Received Date : Sep. 27, 2020 Acceptance Date : Jan. 1, 2021 Published Date : Mar. 1, 2021 Özetçe— COVID-19 pandemic has a dramatic impact on economies and communities all around the world. With social distancing in place and various measures of lockdowns, it becomes significant to understand emotional responses on a great scale. In this paper, a study is presented that determines human emotions during COVID-19 using various machine learning (ML) approaches. To this end, various techniques such as Decision Trees (DT), Support Vector Machines (SVM), k-nearest neighbor (k-NN), Neural Networks (NN) and Naïve Bayes (NB) methods are used in determination of the human emotions. The mentioned techniques are used on a dataset namely Real World Worry dataset (RWWD) that was collected during COVID-19. The dataset, which covers eight emotions on a 9-point scale, grading their anxiety levels about the COVID-19 situation, was collected by using 2500 participants. The performance evaluation of the ML techniques on emotion prediction is carried out by using the accuracy score. Five-fold cross validation technique is also adopted in experiments. The experiment works show that the ML approaches are promising in determining the emotion in COVID- 19 RWWD. -
The Effects of an Emotion Strengthening Training Program On
BALCI ÇELİK / Duyguları Güçlendirme Eğitimi Programı’nın Hemşirelerin ... • 793 Th e Eff ects of an Emotion Strengthening Training Program on the Optimism Level of Nurses Seher BALCI ÇELİK* Abstract Th e aim of this study is to investigate the eff ects of emotion strengthening as a training programme for optimistic for nurses. Th e experimental and control group of this rese- arch has totally composed of 20 nurses. A pre-test post-test research model with control group was been used in this research. Nurses’ optimistic levels have been measured by ‘Optimistic Scale’ which was developed by Balcı and Yılmaz (2002). In order to test that, meaningful diff erences between the scores of pre-test and post-test within both control and experimental group Mann Whitey U and Wilcoxon Signed Rank test were applied for statisical analysis. It was found that this emotional strengthening training had positive eff ects on levels of optimistic for nurses. Th e findings have been discussed in the light of literature, and some suggestions have been made. Key Words Emotion Strengthening Training Program, Nurse, Optimistic, Optimistic Scale. * Correspondence: Ondokuz Mayıs University, Faculty of Education, Department of Education Sciences, Kurupelit 55139 Samsun-Turkey / E-mail: [email protected] Kuram ve Uygulamada Eğitim Bilimleri / Educational Sciences: Th eory & Practice 8 (3) • September 2008 • 793-804 © 2008 Eğitim Danışmanlığı ve Araştırmaları İletişim Hizmetleri Tic. Ltd. Şti. 794 • EDUCATIONAL SCIENCES: THEORY & PRACTICE As internal mechanisms emotions are psychological signs of how an individual feels. Th ey develop in situations which call for an individual’s life style or behavioral pattern. -
Using an Ambiguous Cue Paradigm to Assess Cognitive Bias in Gorillas (Gorilla Gorilla Gorilla) During a Forage Manipulation
ABC 2017, 4(1):70-83 Animal Behavior and Cognition DOI: 10.12966/abc.06.02.2017 ©Attribution 3.0 Unported (CC BY 3.0) Using an Ambiguous Cue Paradigm to Assess Cognitive Bias in Gorillas (Gorilla gorilla gorilla) during a Forage Manipulation Molly C. McGuire1, Jennifer Vonk1*, Grace Fuller2, & Stephanie Allard2 1Oakland University, Department of Psychology, Rochester, MI, USA 2Detroit Zoological Society, Royal Oak, MI USA *Corresponding author (Email: [email protected]) Citation – McGuire, M. C., Vonk, J., Fuller, G., & Allard, S. (2017). Using an ambiguous cue paradigm to assess cognitive bias in gorillas (Gorilla gorilla gorilla) during a forage manipulation. Animal Behavior and Cognition, 4(1), 70–83. doi: 10.12966/abc.06.02.2017 Abstract - In nonhumans, ‘optimism’ is often defined as responding to an ambiguous item in the same manner as to items previously associated with reward (or lack of punishment), and “pessimism” is defined as responding to an ambiguous item in the same manner as to items previously associated with a lack of reward (or with punishment). We measured the degree of “optimism” and “pessimism” in three captive male western lowland gorillas (Gorilla gorilla gorilla) during four consecutive two-week periods in which the amount of available forage material (mulberry, Moraceae or alfalfa, Medicago sativa) was manipulated. We assessed cognitive bias using an ambiguous cue paradigm for the first time. Pairs of two-dimensional shapes were presented on a touch-screen computer in a forced choice task in which one shape was always reinforced (P), one was never reinforced (N), and one was reinforced half the time, making it ambiguous (A). -
The Somatic Marker Hypothesis: a Neural Theory of Economic Decision
Games and Economic Behavior 52 (2005) 336–372 www.elsevier.com/locate/geb The somatic marker hypothesis: A neural theory of economic decision Antoine Bechara ∗, Antonio R. Damasio Department of Neurology, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA Received 8 June 2004 Available online 23 September 2004 Abstract Modern economic theory ignores the influence of emotions on decision-making. Emerging neuro- science evidence suggests that sound and rational decision making, in fact, depends on prior accurate emotional processing. The somatic marker hypothesis provides a systems-level neuroanatomical and cognitive framework for decision-making and its influence by emotion. The key idea of this hypothe- sis is that decision-making is a process that is influenced by marker signals that arise in bioregulatory processes, including those that express themselves in emotions and feelings. This influence can oc- cur at multiple levels of operation, some of which occur consciously, and some of which occur non-consciously. Here we review studies that confirm various predictions from the hypothesis, and propose a neural model for economic decision, in which emotions are a major factor in the interac- tion between environmental conditions and human decision processes, with these emotional systems providing valuable implicit or explicit knowledge for making fast and advantageous decisions. 2004 Elsevier Inc. All rights reserved. Introduction Modern economic theory assumes that human decision-making involves rational Bayesian maximization of expected utility, as if humans were equipped with unlim- ited knowledge, time, and information-processing power. The influence of emotions on * Corresponding author. E-mail address: [email protected] (A. -
Emotion Classification Based on Biophysical Signals and Machine Learning Techniques
S S symmetry Article Emotion Classification Based on Biophysical Signals and Machine Learning Techniques Oana Bălan 1,* , Gabriela Moise 2 , Livia Petrescu 3 , Alin Moldoveanu 1 , Marius Leordeanu 1 and Florica Moldoveanu 1 1 Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Bucharest 060042, Romania; [email protected] (A.M.); [email protected] (M.L.); fl[email protected] (F.M.) 2 Department of Computer Science, Information Technology, Mathematics and Physics (ITIMF), Petroleum-Gas University of Ploiesti, Ploiesti 100680, Romania; [email protected] 3 Faculty of Biology, University of Bucharest, Bucharest 030014, Romania; [email protected] * Correspondence: [email protected]; Tel.: +40722276571 Received: 12 November 2019; Accepted: 18 December 2019; Published: 20 December 2019 Abstract: Emotions constitute an indispensable component of our everyday life. They consist of conscious mental reactions towards objects or situations and are associated with various physiological, behavioral, and cognitive changes. In this paper, we propose a comparative analysis between different machine learning and deep learning techniques, with and without feature selection, for binarily classifying the six basic emotions, namely anger, disgust, fear, joy, sadness, and surprise, into two symmetrical categorical classes (emotion and no emotion), using the physiological recordings and subjective ratings of valence, arousal, and dominance from the DEAP (Dataset for Emotion Analysis using EEG, Physiological and Video Signals) database. The results showed that the maximum classification accuracies for each emotion were: anger: 98.02%, joy:100%, surprise: 96%, disgust: 95%, fear: 90.75%, and sadness: 90.08%. In the case of four emotions (anger, disgust, fear, and sadness), the classification accuracies were higher without feature selection. -
Research-Based Practice with Women Who Have Had Miscarriages
CMcal Scholarship Research- based Practice with Women Who Have Had Miscarriages Kristen M. Swanson Purpose: To summarize a research-based description of what it is like to miscarry and to recommend an empirically tested theory of caring for women who have experienced miscarriage. Design: The research program included three phases: interpretive theory generation, descriptive survey and instrument development, and experimental testing of a theory-based intervention. Methods: Research methods included interpretive phenomenologE factor analysis, and ANCOVA. Findings: A theory of caring and a model of what it is like to miscarry were generated, refined, and tested. A case study shows one woman’s response to miscarrying and illustrates clinical application of the caring theory. Conclusions: The Miscarriage Model is a useful framework for anticipating the variety of responses women have to miscarrying. The caring theory is an effective and sensitive guide to clinical practice with women who miscarry. IMAGE:JOURNAL OF NURSINGSCHOLARSHIP, 1999; 31 :4,339-345.01999 SIGMA THETATAU INTERNATIONAL. [Key words: caring, miscarriage, theory construction, counseling] t least one in five pregnancies ends in miscarriage-the program of inquiry about miscarriage and its aftermath. An unplanned, unexpected ending of pregnancy before the important contribution of the pilot study was the conclusion that time of expected fetal viability (Hall, Beresford, & a woman’s feelings about miscarriage could be understood only Quinones, 1987). Women’s responses range from relief in the context of what being pregnant and having a miscarriage to devastation with much variability in the time required meant to her. For example, if being pregnant was perceived as a Ato achieve resolution. -
DEAP: a Database for Emotion Analysis Using Physiological Signals
IEEE TRANS. AFFECTIVE COMPUTING 1 DEAP: A Database for Emotion Analysis using Physiological Signals Sander Koelstra, Student Member, IEEE, Christian M¨uhl, Mohammad Soleymani, Student Member, IEEE, Jong-Seok Lee, Member, IEEE, Ashkan Yazdani, Touradj Ebrahimi, Member, IEEE, Thierry Pun, Member, IEEE, Anton Nijholt, Member, IEEE, Ioannis Patras, Member, IEEE Abstract—We present a multimodal dataset for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection and an online assessment tool. An extensive analysis of the participants’ ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants’ ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence and like/dislike ratings using the modalities of EEG, peripheral physiological signals and multimedia content analysis. Finally, decision fusion of the classification results from the different modalities is performed. The dataset is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods. Index Terms—Emotion classification, EEG, Physiological signals, Signal processing, Pattern classification, Affective computing. ✦ 1 INTRODUCTION information retrieval. Affective characteristics of multi- media are important features for describing multime- MOTION is a psycho-physiological process triggered dia content and can be presented by such emotional by conscious and/or unconscious perception of an E tags. -
Emotion Classification Based on Brain Wave: a Survey
Li et al. Hum. Cent. Comput. Inf. Sci. (2019) 9:42 https://doi.org/10.1186/s13673-019-0201-x REVIEW Open Access Emotion classifcation based on brain wave: a survey Ting‑Mei Li1, Han‑Chieh Chao1,2* and Jianming Zhang3 *Correspondence: [email protected] Abstract 1 Department of Electrical Brain wave emotion analysis is the most novel method of emotion analysis at present. Engineering, National Dong Hwa University, Hualien, With the progress of brain science, it is found that human emotions are produced by Taiwan the brain. As a result, many brain‑wave emotion related applications appear. However, Full list of author information the analysis of brain wave emotion improves the difculty of analysis because of the is available at the end of the article complexity of human emotion. Many researchers used diferent classifcation methods and proposed methods for the classifcation of brain wave emotions. In this paper, we investigate the existing methods of brain wave emotion classifcation and describe various classifcation methods. Keywords: EEG, Emotion, Classifcation Introduction Brain science has shown that human emotions are controlled by the brain [1]. Te brain produces brain waves as it transmits messages. Brain wave data is one of the biological messages, and biological messages usually have emotion features. Te feature of emo- tion can be extracted through the analysis of brain wave messages. But because of the environmental background and cultural diferences of human growth, the complexity of human emotion is caused. Terefore, in the analysis of brain wave emotion, classifcation algorithm is very important. In this article, we will focus on the classifcation of brain wave emotions.