Analysis of Machine Learning Algorithms for the Recognition of Basic Emotions: Data Mining of Psychophysiological Sensor Information
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
1 COPYRIGHT STATEMENT This Copy of the Thesis Has Been
University of Plymouth PEARL https://pearl.plymouth.ac.uk 04 University of Plymouth Research Theses 01 Research Theses Main Collection 2012 Life Expansion: Toward an Artistic, Design-Based Theory of the Transhuman / Posthuman Vita-More, Natasha http://hdl.handle.net/10026.1/1182 University of Plymouth All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. COPYRIGHT STATEMENT This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognize that its copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without the author’s prior consent. 1 Life Expansion: Toward an Artistic, Design-Based Theory of the Transhuman / Posthuman by NATASHA VITA-MORE A thesis submitted to the University of Plymouth in partial fulfillment for the degree of DOCTOR OF PHILOSOPHY School of Art & Media Faculty of Arts April 2012 2 Natasha Vita-More Life Expansion: Toward an Artistic, Design-Based Theory of the Transhuman / Posthuman The thesis’ study of life expansion proposes a framework for artistic, design-based approaches concerned with prolonging human life and sustaining personal identity. To delineate the topic: life expansion means increasing the length of time a person is alive and diversifying the matter in which a person exists. -
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. -
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. -
Spangler Ku 0099D 11237 DA
Abstract This study sought to gain a deeper understanding of the types of benefits, programs, and organizational practices employers currently are providing to prevent distress among employees or to help employees become more resilient to adverse conditions. Forty-six employer representatives discussed the perceived strengths of their organizations’ approaches during interviews and discussion groups. Grounded theory methodology was employed to sample and analyze these data. Based on patterns that emerged from the narratives of these participants, a model is proposed to explain three effective approaches used by employers in addressing stress in the workplace: 1) preventing stress/building resilience, 2) providing information, resources, and benefits to employees, and 3) intervening actively with troubled employees. Trust, both in relationships and in organizational structures, emerged as a core concept explaining effectiveness of these approaches. This model may be used to frame future strategies used by employers to support healthy engaged employees and to guide investigations into social and emotional aspects related to work. iii Acknowledgments Finishing a dissertation feels similar to climbing a mountain – looking back on where you have been makes you feel a little weary, but the view from the top is quite exhilarating and immensely satisfying. You also see there are other peaks left to master. I offer warm gratitude to many people who helped me during my climb. To all of the professors over the years who created opportunities for thinking deeply and pondering life’s important questions. To Lisa Mische Lawson for helping to chart my course of study over the years in the Therapeutic Science curriculum and advice during the dissertation process. -
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. -
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. -
Stress, Cortison Und Homöostase
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by RERO DOC Digital Library N.T.M. 18 (2010) 169–195 0036-6978/10/020169-27 DOI 10.1007/s00048-010-0017-2 Published online: 10 August 2010 RTICLES PRINGER ASEL Ó 2010 S B AG /A . RTIKEL A Stress, Cortison und Homöostase Künstliche Nebennierenrindenhormone und physiologisches Gleichgewicht, 1936–1960 Lea Haller Stress, Cortisone and Homeostasis. Adrenal Cortex Hormones and Physiological Equilibrium, 1936–1960 This article investigates the emergence of the concept of stress in the 1930s and outlines its changing disciplinary and conceptual frames up until 1960. Originally stress was a physio- logical concept applied to the hormonal regulation of the body under stressful conditions. Correlated closely with chemical research into corticosteroids for more than a decade, the stress concept finally became a topic in cognitive psychology. One reason for this shift of the concept to another discipline was the fact that the hormones previously linked to the stress concept were successfully transferred from laboratory to medical practice and adopted by disciplines such as rheumatology and dermatology. Thus the stress concept was dissociated from its hormonal context and became a handy formula that allowed postindustrial soci- ety to conceive of stress as a matter of individual concern. From a physiological phenomenon stress turned into an object of psychological discourse and individual coping strategies. Keywords: Stress, general adaptation syndrome, hormones of the adrenal cortex, corti- sone therapy, coping Schlüsselwörter: Stress, Allgemeines Adaptationssyndrom, Nebennierenrindenhormone, Cortisontherapie, Stressbewältigung ,,No concept in the modern psychological, sociological, or psychiatric literature is more extensively studied than stress.’’ Stevan E. -
Cinemeducation Movies Have Long Been Utilized to Highlight Varied
Cinemeducation Movies have long been utilized to highlight varied areas in the field of psychiatry, including the role of the psychiatrist, issues in medical ethics, and the stigma toward people with mental illness. Furthermore, courses designed to teach psychopathology to trainees have traditionally used examples from art and literature to emphasize major teaching points. The integration of creative methods to teach psychiatry residents is essential as course directors are met with the challenge of captivating trainees with increasing demands on time and resources. Teachers must continue to strive to create learning environments that give residents opportunities to apply, analyze, synthesize, and evaluate information (1). To reach this goal, the use of film for teaching may have advantages over traditional didactics. Films are efficient, as they present a controlled patient scenario that can be used repeatedly from year to year. Psychiatry residency curricula that have incorporated viewing contemporary films were found to be useful and enjoyable pertaining to the field of psychiatry in general (2) as well as specific issues within psychiatry, such as acculturation (3). The construction of a formal movie club has also been shown to be a novel way to teach psychiatry residents various aspects of psychiatry (4). Introducing REDRUMTM Building on Kalra et al. (4), we created REDRUMTM (Reviewing [Mental] Disorders with a Reverent Understanding of the Macabre), a Psychopathology curriculum for PGY-1 and -2 residents at Rutgers Robert Wood Johnson Medical School. REDRUMTM teaches topics in mental illnesses by use of the horror genre. We chose this genre in part because of its immense popularity; the tropes that are portrayed resonate with people at an unconscious level. -
EMOTION CLASSIFICATION of COVERED FACES 1 WHO CAN READ YOUR FACIAL EXPRESSION? a Comparison of Humans and Machine Learning Class
journal name manuscript No. (will be inserted by the editor) 1 A Comparison of Humans and Machine Learning 2 Classifiers Detecting Emotion from Faces of People 3 with Different Coverings 4 Harisu Abdullahi Shehu · Will N. 5 Browne · Hedwig Eisenbarth 6 7 Received: 9th August 2021 / Accepted: date 8 Abstract Partial face coverings such as sunglasses and face masks uninten- 9 tionally obscure facial expressions, causing a loss of accuracy when humans 10 and computer systems attempt to categorise emotion. Experimental Psychol- 11 ogy would benefit from understanding how computational systems differ from 12 humans when categorising emotions as automated recognition systems become 13 available. We investigated the impact of sunglasses and different face masks on 14 the ability to categorize emotional facial expressions in humans and computer 15 systems to directly compare accuracy in a naturalistic context. Computer sys- 16 tems, represented by the VGG19 deep learning algorithm, and humans assessed 17 images of people with varying emotional facial expressions and with four dif- 18 ferent types of coverings, i.e. unmasked, with a mask covering lower-face, a 19 partial mask with transparent mouth window, and with sunglasses. Further- Harisu Abdullahi Shehu (Corresponding author) School of Engineering and Computer Science, Victoria University of Wellington, 6012 Wellington E-mail: [email protected] ORCID: 0000-0002-9689-3290 Will N. Browne School of Electrical Engineering and Robotics, Queensland University of Technology, Bris- bane QLD 4000, Australia Tel: +61 45 2468 148 E-mail: [email protected] ORCID: 0000-0001-8979-2224 Hedwig Eisenbarth School of Psychology, Victoria University of Wellington, 6012 Wellington Tel: +64 4 463 9541 E-mail: [email protected] ORCID: 0000-0002-0521-2630 2 Shehu H. -
Commemorating a Classic, and Its Creator Walter Bradford Cannon
Indian J Physiol Pharmacol 1998; 42(4) : 437-439 Editorial Commemorating a Classic, and its Creator Walter Bradford Cannon Some papers are born great, some become great, and some have greatness thrust upon them. We have decided to thrust greatness on a paper published 100 years ago in Volume 1 of the American Journal of Physiology (1). Not that the paper is not good, but of far greater interest to us is its author. But first, the paper: it described for the first time the use of X-rays for the study of gastric motility. The study was done on cats who had been given food mixed with the radio-opaque material, 'bismuth subnitrate. By following the changes in shape of the shadows on a f1uorescent screen, Cannon deduced a wealth of information. He described the shape of the stomach, the frequency and nature of contractions, possible function of contractions and the rate and pattern of gastric emptying. By observing that food cornered in the fundus remains undisturbed for quite some time, he concluded that digestion by salivary amylase may continue in the fundus. He confirmed it by measuring the pH of different 'layers' of food in the fundus. The pH was alkaline in the food extracted from a dept of two and half centimetres one and a half hour after ingestion. He also described the differences in handling of foods of different consistencies by the stomach. Further, by observing absence of peristalsis in cats that went into rage during recording (generally, male cats !), he concluded that anger inhibits gastric emptying. -
Hybrid Approach for Emotion Classification of Audio Conversation Based on Text and Speech Mining
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 46 ( 2015 ) 635 – 643 International Conference on Information and Communication Technologies (ICICT 2014) Hybrid Approach for Emotion Classification of Audio Conversation Based on Text and Speech Mining a, a b Jasmine Bhaskar * ,Sruthi K , Prema Nedungadi aDepartment of Compute science , Amrita University, Amrita School of Engineering, Amritapuri, 690525, India bAmrita Create, Amrita University, Amrita School of Engineering, Amritapuri, 690525, India Abstract One of the greatest challenges in speech technology is estimating the speaker’s emotion. Most of the existing approaches concentrate either on audio or text features. In this work, we propose a novel approach for emotion classification of audio conversation based on both speech and text. The novelty in this approach is in the choice of features and the generation of a single feature vector for classification. Our main intention is to increase the accuracy of emotion classification of speech by considering both audio and text features. In this work we use standard methods such as Natural Language Processing, Support Vector Machines, WordNet Affect and SentiWordNet. The dataset for this work have been taken from Semval -2007 and eNTERFACE'05 EMOTION Database. © 20152014 PublishedThe Authors. by ElsevierPublished B.V. by ElsevierThis is an B.V. open access article under the CC BY-NC-ND license (Peer-reviewhttp://creativecommons.org/licenses/by-nc-nd/4.0/ under responsibility of organizing committee). of the International Conference on Information and Communication PeerTechnologies-review under (ICICT responsibility 2014). of organizing committee of the International Conference on Information and Communication Technologies (ICICT 2014) Keywords: Emotion classification;text mining;speech mining;hybrid approach 1.