DOCSLIB.ORG

Download Article

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Download Article Advances in Intelligent Systems Research, volume 166 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019) Summarizing Emotions from Text Using Plutchik’s Wheel of Emotions Mohsin Manshad Abbasi Anatoly Beltiukov Theoratical Foundation of Computer Sciences Theoratical Foundation of Computer Sciences Udmurt State University Udmurt State University Izhevsk, Russian Federation Izhevsk, Russian Federation [email protected] [email protected] Abstract—Text is an important and major source of shopping. We will analyze and summarize the emotions from communication over Internet. It is analyzed to identify text using the concept of Plutchik’s wheel of emotions [1]. In interesting information and trends of communication. Within 1980’s Robert Plutchik divided emotions into eight main this work, we are analyzing emotions expressed by people on categories. Half of these emotions are positive emotions, and Internet using Plutchik’s wheel of emotions. Plutchik’s wheel of the other half are negative ones. They are seen as opposite to emotions is use as a tool to identify and summarize emotions to each other. We can observe this among secondary emotions, their primary classes. To accomplish it, we allocate a weight to such as joy is opposite to sadness, surprise is opposite to each emotion depending upon the class it belongs and its anticipation, trust is opposite to disgust, and anger is opposite distance from the center of Plutchik’s wheel of emotions. These to fear. He explained each emotion in detail and divided it weights are then multiplied by the frequencies of emotions in text to identify their intensity level. The intensity of each into subgroups, treating them as secondary and tertiary emotion is summed up with the intensity of its primary emotion emotions in a wheel-shaped mechanism. His work describes while summarizing it. We observed that our mechanism a very interesting relationship between emotions, their effectively summarize emotions from text. This paper is divided intensities and polarities. He also noticed that the intensity of into several sections. The methodology, results and conclusions an emotion is high when lies in the center of the wheel and it are discussed in detail in their respective sections. decreases as the distance from the center increases. Keywords—Plutchik wheel, emotion, class, summarization, internet, blogs, communication I. INTRODUCTION Text analysis is the subject of major research over the recent decades. This is due to availability of online and offline data. The online text is growing at a very high pace. There is a need to develop tools and methods for solving text analysis problems. Text analysis has a lot of applications. It can be used to detect fraud in online transactions, predict the future, identify groups and people, etc. The theme of our research is to develop methods that can be used to analyze emotions from a text efficiently and then use them for classification of the text and its summarization. Today we have various social networks, blogs, and groups where people can express and convey their emotions to others. This emotion transfer can be constructive if it is Fig. 1. Plutchik wheel of emotions positive, and can be destructive if it is negative. There are still challenges in text analysis such as identifying a hate group online using text analysis. This is a question that still II. HISTORY OF EMOTION ANALYSIS requires efforts. Emotions from a text are used to determine The analysis of emotions was began with the public opinion about a topic and to predict a future event development of the very first general survey system in 1966. based on it, such as election results, reaction to governmental It was started by Philip Stone at University of Harvard, and policies, etc. The need of such system was seriously felt it was probably the first stage of text analysis and during the crisis of the Middle East, where governments identification of emotions from it [2]. After that, text failed to understand the people’s emotions. During these analysis in different languages started gradually. Several events people used the online text as the major source to scientist made remarkable contributions in text processing. organize protests against the government and its policies. Important among them was the contribution of Jayne Wiebe, Similarly, American elections were linked to online groups Peter Turney and Vasileios Hatzivassiloglou in the early and people who spread propaganda. 1990s. In 1990, Jane Wiebe defined the term “subjectivity” In this paper, we will use a text taken from an online blog for researching information retrieval [3]. Later, in 1997 in which people expressed their emotions about online Hatzivassiloglou revealed semantic orientation of adjectives Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). 291 Advances in Intelligent Systems Research, volume 166 [4]. A few years later, in 2002, Peter Turney offered his 퐼푛푡푒푛푠푡푦 표푓 퐸푚표푡표푛 퐼퐸푖 = 퐶푖 × 푊푖 () revolutionary approach of “Thumbs up” and “Thumbs down” for positive and negative review of a text [5]. During 푊푒푔ℎ푡 표푓 푃푟푚푎푟푦 퐸푚표푡표푛 푊푖 푝푟푖푚푒푚표 푢푝푑푎푡푒푑 = the same year Pang proposed the mood lexicon manually for 푊푖 푝푟푖푚푒푚표 + 퐼퐸푖 () text analysis [6]. In 2009, Denecke reported an interesting study conducted in several areas to demonstrate usefulness V. EXPERIMENT & RESULTS of previous polarity estimates from SentiWordNet [7]. Recently the scientists started using different data mining We applied our methodology on the text to identify tools, statistical methods, supervised, semi-supervised emotions using Plutchik’s wheel of emotions. We observed learning techniques and combinations to identify and a list of emotions from the text with different polarities. analyze emotions from a text. First, we searched for positive emotions, counted their frequencies and identified their weights. On multiplying the III. RELATED WORK frequency of an emotion by its weight we observed the Going beyond polarity in sentiment analysis is currently intensity of a particular emotion in the text. On summing up not studied. However, several examples can be found where the intensities of all the positive emotions together we got an methods are used to collect more information than just overall intensity of positive emotions in the text as described polarity, such as the use of recursive auto-coder is to predict in table I below. mood distributions in different dimensions [8]. The authors TABLE I. POSITIVE EMOTIONS IN TEXT also promote efficient calculations using a structure they called SenticNet [9,10]. Sentimental dimensions of this Positive emotions in the text Emotions structure are modeled in the hourglass model, which is a Frequency Weight Value derivative of Plutchik’s emotion wheel [1]. In 2012 the Trust 15 3 45 author gathered a large collection of tweets and experimented with self-signed hash tags of emotions [11]. Surprise 12 3 36 It is difficult to define standards for emotion analysis Interest 9 2 18 from a text because emotions are usually subjective and Amazement 5 4 20 cannot be clearly defined. The works of Parrott, Plutchik and Schroder aimed at defining standards in this area by Acceptance 4 2 8 determining the minimum set of basic emotions from which Love 5 2 10 complex emotions can be constructed [12, 1, 13]. In 2012 the authors developed methods for emotion reasoning [14]. In Awesome 7 1 7 our work we are using Plutchik’s wheel of emotions to identify, analyze and summarize emotions from text. From the above analysis, the most frequent positive IV. METHODOLOGY emotions in the text can be observed. We can also sum up the frequencies of all positive emotions to get an overall In this work, we are purposing a new methodology that frequency of positive emotions in text. The results of the uses Plutchik’s wheel of emotions for identification and above analysis are further elaborated using a graph plotted analysis of emotions from a text. The work begins with the below in Fig. 2. Positive emotions are represented with green selection of a text from an online blog. In this blog people colour whereas the blue colour represents the emotion expressed their opinion about shopping online. Our system ‘Awesome’ that, according to Plutchik, is a feeling aroused will be engaged in mining and identifying emotions from the because of surprise and fear that makes it a neutral emotion. text. Each emotion in Plutchik’s wheel of emotion will be given a certain weight depending upon its intensity. Positive Emotion Using Plutchik's Wheel We will begin by identifying the most intense emotions from the text that are close to the center of Plutchik’s wheel 20 and then broaden up the search for its secondary and tertiary 15 emotions along the radius of the wheel. The weight of the emotions close to the center of the wheel is 4, which is also 10 the maximum level of intensity in Plutchik’s wheel of emotions. For the emotions in second layer of Plutchik wheel 5 of emotions, the weight is 3 and it gradually decreases by l from layer to layer of Plutchik’s wheel of emotions as we 0 move far from the center. We will count the total occurrences 1 of an emotion in text 퐶푖 as in (1). Then by multiplying it with the weight of an emotion푊푖, we get the intensity 퐼퐸푖 of Trust surprise interest each emotion in the text as in (2). The intensity of an emotion amazement acceptance love is added to the weight of primary emotion 푊푖 푝푟푖푚푒푚표 and using this mechanism the weights of all primary emotions are awesome updated as in (3). Fig. 2. Positive emotion of Plutchik’s wheel identified in the text 푛 퐸푚표푡표푛 퐶표푢푛푡 퐶푖 = ∑푗=1 푂푐푐푢푟푒푛푐푒 표푓 퐸푚표푡표푛푖푗 () 292 Advances in Intelligent Systems Research, volume 166 Similarly, we observed negative emotions in the text, Plutchik’s wheel that lies close to the center as represented counted their frequencies and identified their weights.
Load more

Recommended publications
  • An Analysis of Annotated Corpora for Emotion Classification in Text
    An Analysis of Annotated Corpora for Emotion Classification in Text Laura-Ana-Maria Bostan and Roman Klinger Institut fur¨ Maschinelle Sprachverarbeitung University of Stuttgart, Pfaffenwaldring 5b, 70569 Stuttgart, Germany [email protected] [email protected] Abstract Several datasets have been annotated and published for classification of emotions. They differ in several ways: (1) the use of different annotation schemata (e. g., discrete label sets, including joy, anger, fear, or sadness or continuous values including valence, or arousal), (2) the domain, and, (3) the file formats. This leads to several research gaps: supervised models often only use a limited set of available resources. Additionally, no previous work has compared emotion corpora in a systematic manner. We aim at contributing to this situation with a survey of the datasets, and aggregate them in a common file format with a common annotation schema. Based on this aggregation, we perform the first cross-corpus classification experiments in the spirit of future research enabled by this paper, in order to gain insight and a better understanding of differences of models inferred from the data. This work also simplifies the choice of the most appropriate resources for developing a model for a novel domain. One result from our analysis is that a subset of corpora is better classified with models trained on a different corpus. For none of the corpora, training on all data altogether is better than using a subselection of the resources. Our unified corpus is available at http://www.ims.uni-stuttgart.de/data/unifyemotion. Title and Abstract in German Eine Analyse von annotierten Korpora zur Emotionsklassifizierung in Text Es existieren bereits verschiedene Textkorpora, welche zur Erstellung von Modellen fur¨ die automatische Emotionsklassifikation erstellt wurden.
    [Show full text]
  • A Survey on Performance Evaluation of Emotion Recognition on Unison Model with LSSVM Classifier Miss
    IJRECE VOL. 7 ISSUE 2 (APRIL- JUNE 2019) ISSN: 2393-9028 (PRINT) | ISSN: 2348-2281 (ONLINE) A Survey on Performance Evaluation of Emotion Recognition on Unison Model with LSSVM Classifier Miss. Mayuri Nikam, Sheetal Thokal Department of Computer Engineering, JSPM’S Imperial college of Engineering and Research, Wagholi Pune. Abstract- The analysis of social networks is a very challenging categorization with two additional emotions and presented his research area while a fundamental aspect concerns the detection categorization in a wheel of emotions. Finally, Profile of Mood of user communities. The existing work of emotion recognition States (POMS) is a psychological instrument that defines a six- on Twitter specifically depends on the use of lexicons and dimensional mood state representation using text mining. The simple classifiers on bag-of words models. The vital question of novel algorithm a Profile of Mood States (POMS) generating our observation is whether or not we will enhance their overall twelve-dimensional mood state representation using 65 performance using machine learning algorithms. The novel adjectives with combination of Ekman’s and Plutchik’s algorithm a Profile of Mood States (POMS) represents twelve- emotions categories like, anger, depression, fatigue, vigour, dimensional mood state representation using 65 adjectives with tension, confusion, joy, disgust, fear, trust, surprise and combination of Ekman’s and Plutchik’s emotions categories anticipation. Previous work generally studied only one emotion like, anger, depression, fatigue, vigour, tension, confusion, joy, classification. Working with multiple classifications disgust, fear, trust, surprise and anticipation. These emotions simultaneously not only enables performance comparisons classify with the help of text based bag-of-words and LSI between different emotion categorizations on the same type of algorithms.
    [Show full text]
  • LNU-THESIS-2016.Pdf (2.995Mb)
    REAL TIME CLASSIFICATION OF EMOTIONS TO CONTROL STAGE LIGHTING DURING DANCE PERFORMANCE A Thesis Presented to The Faculty of the Department of Biomedical Engineering University of Houston In Partial Fulfillment of the Requirements for the Degree Master of Science In Biomedical Engineering By Shruti Ray August 2016 REAL TIME CLASSIFICATION OF EMOTIONS TO CONTROL STAGE LIGHTING DURING DANCE PERFORMANCE _____________________________ Shruti Ray Approved: ________________________________ Chair of The Committee Dr. Jose Luis Contreras – Vidal, Professor, Department of Electrical and Computer Engineering Committee Members: ________________________________ Dr. Ahmet Omurtag, Associate Professor, Department of Biomedical Engineering _______________________________ Dr. Saurabh Prasad, Assistant Professor, Department of Electrical and Computer Engineering, _______________________ ____________________________ Dr. Suresh K. Khator, Dr. Metin Akay, Founding Chair, Associate Dean John S. Dunn Cullen Endowed Professor, Cullen College of Engineering Department of Biomedical Engineering Acknowledgement I would like to show my deepest gratitude for my advisor, Dr. Jose Luis Contreras - Vidal, for his continuous guidance, encouragement and support throughout this research project. I would also like to thank my colleagues from Laboratory for Noninvasive Brain- Machine Interface Systems, for their immense support and encouragement and help in data collection for analysis. I would like to thank Ms. Rebecca B. Valls and Ms. Anastasiya Kopteva for their dancer performances with EEG caps to help me with the data collection. Additionally, I would like to thank all my friends Su Liu, Thomas Potter, Dr. Kinjal Dhar Gupta and my sister Shreya Ray who have supported me in both happy and adverse conditions. Last, but not the least, I would like to thank my parents and family to believe in my dreams and supporting my quest for higher education.
    [Show full text]
  • Spin the Wheel of Emotions Well-Being Level 4-6
    Emotional Grade Spin the Wheel of Emotions Well-Being Level 4-6 Materials Access to the internet Learning Recognize a variety of emotions including opposite emotions, mixed Outcome emotions, and intensities of emotions. Description Ask the child to name as many emotions as possible. After they have done so, visit Plutchik’s Wheel of Emotions webpage and look at the wheel of emotions together. Review each section of the wheel and think about what the sections have in common. Observe the emotions with no colour and guess what they may mean. Read all of the emotions and provide a definition for any that the child does not know. After reviewing the Wheel of Emotions, share with the child that Robert Plutchik was a psychologist that stated there are eight basic emotions: joy, trust, fear, surprise, sadness, anticipation, anger, and disgust. Each basic emotion has a polar opposite. This means: Joy is the opposite of sadness Fear is the opposite of anger Anticipation is the opposite of surprise Disgust is the opposite of trust Look at the webpage again and explain to the child that the emotions with no colour represent an emotion that is a mix of two of the basic emotions. For example, the emotions of anticipation and joy combine to be the emotion of optimism. Also, explain that emotions get more intense as they move from the outside of the wheel to the center of the wheel. You can see this represented on the wheel with the darker shades representing the most intense emotions. After looking at the wheel again, ask the child: What do you think
    [Show full text]
  • Plutchik's Wheel of Emotions Cheat Sheet by [Deleted] Via Cheatography.Com/2754/Cs/6051
    Plutchik's Wheel of Emotions Cheat Sheet by [deleted] via cheatography.com/2754/cs/6051/ Intoduc​ tion Plutchik's Wheel of Emotions In 1980 Robert Plutchik constru​ cted a wheel-like diagram of emotions, an infograph that uses the color wheel to illustrate variations in human affect and the relatio​ nship among emotions. Current applica​ tions of the wheel of emotion include robotics and sentiment analysis. Psychol​ ogist Robert Plutchik created the 2D wheel and a conical 3D version in 1980 as a tool for underst​ anding his psychoe​ vol​ uti​ onary theory of emotion. Plutchik identified eight primary emotions, which he coordin​ ated in pairs of opposites (see table). Intensity of emotion and indicator color increases toward the center of the wheel and decreases outward. At the center, terror, for example, becomes fear and then apprehe​ nsion; ecstasy becomes joy and then serenity. Secondary emotions are displayed as combin‐​ ations of the primary ones. ConveyAPI, a web service that interfaces with a text analysis engine, Eight Basic Emotions applies the wheel of emotions to sentiment analysis for social media Human Feelings Emoti​ ons Oppos​ ite monitor​ ing. ConveyAPI uses the concepts of Plutchik's wheel of Optimism Anticip​ ation + Joy Disappr​ oval emotions in text analysis to identify the polarity (positive or negative) Love Joy + Trust Remorse and attribute specific emotions and their intensity. Submission Trust + Fear Contempt credit: http://​ wha​ tis​ .te​ cht​ arg​ et.com​ /de​ fin​ iti​ on/​ Plu​ tch​ iks​ -Wh​ eel​ -of​ -E‐​ motions Awe Fear + Surprise Aggression Disappr​ oval Surprise + Sadness Optimism Basic Emotions & Opposites Remorse Sadness + Disgust Love Basic Emotion Emoti​ onal Opposite Contempt Disgust + Anger Submission Joy Sadness Aggress​ ive​ ness Anger + Anticip​ ation Awe Trust Disgust Fear Anger Surprise Anticip​ ation By [deleted] Published 12th November, 2015.
    [Show full text]
  • 31 an Empirical Assessment of the Role of Emotions and Behavior in Conflict Using Automatically Generated Data* Abstract Keyword
    V2, N2, Jul. 2013, 31-47 An Empirical Assessment of the Role of Emotions and Behavior in Conflict Using Automatically Generated Data* Steve Shellman Sean O’Brien Strategic Analysis Enterprises (SAE), Inc. Abstract Automated event data extraction techniques have revolutionized the study of conflict dynamics through the ability of these techniques to generate large volumes of timely data measuring dynamic interactions among actors around the world. In this paper, we describe our approach for adapting these techniques to extract data on sentiments and emotions, which are theorized to crucially contribute to escalating and de-escalating conflict. Political scientists view political conflict as resulting from a series of strategic interactions between groups and individuals. Psychologists highlight additional factors in political conflict, such as endorsements and condemnations, the public’s attitude toward its leaders, the impact of public attitudes on policy, and decisions to engage in armed conflict. This project combines these two approaches to examine hypotheses regarding the effects that different emotional impulses have on government and dissident decisions to escalate or de-escalate their use of hostility and violence. Across the two cases examined—the democratic Philippines and authoritative Egypt between 2001 and 2012—we found consistent evidence that intense societal fear of dissidents and societal disgust toward the government were associated with increases in dissident hostility. Conversely, societal anger toward dissidents was associated with a reduction in dissident hostility. However, we also found noticeable differences between the two regimes. We close the article with a summary of these similarities and differences, along with an assessment of their implications for future conflict studies.
    [Show full text]
  • Testing Your Emotions
    TESTING YOUR EMOTIONS AND HOW YOU CAN APPLY SOME PERSONAL LEADERSHIP TO KEEP THEM UNDER CONTROL… THANKS TO…. • Robert Plutchik. • Hugo Lövheim. • Jerry Weinberg. • Michael Bolton. • And many more…. • And some cats. WHY? BECAUSE I BELIEVE THAT UNDERSTANDING OUR EMOTIONS MAKES US BETTER TESTERS EMOTIONS ARE BASIC HUMAN RESPONSES WE ALL EXPERIENCE EMOTIONS EMOTIONS CAN HAVE A POWERFUL IMPACT ON MEMORY WITHOUT EMOTION, WE DON’T REASON WELL. ANTONIO DAMASIO, “THE FEELING OF WHAT HAPPENS”. FRUSTRATION – A PERSONAL STORY WHAT ARE EMOTIONS? The word "emotion" dates back to 1579, when it was adapted from the French word émouvoir, which means "to stir up". However, the earliest precursors of the word likely dates back to the very origins of language. Wikipedia WHAT ARE EMOTIONS? • Discrete and consistent responses to internal or external events which have a particular significance for the organism. • Brief in duration and consist of a coordinated set of responses, which may include verbal, physiological, behavioral, and neural mechanisms. • Biologically given and a result of evolution because they provided good solutions to ancient and recurring problems that faced our ancestors. WHAT AREN’T THEY? • Feelings - a subjective representation of emotions, private to the individual experiencing them. • Moods - diffuse affective states that generally last for much longer durations than emotions and are also usually less intense than emotions. • Affects - a term that describe the topics of emotion, feelings, and moods together. HOW CAN WE UNDERSTAND? • Think about the last time you received an emotional response - How did it make you feel? • Think about the last time you felt “emotional” - Why was it? How did it make you feel? Did you try and hide it? SO WHAT ABOUT THESE? Source: Kerr Photography - http://www.flickr.com/photos/23992930@N04/4633061196/ OR THIS? Source: Michelle Tribe - http://www.flickr.com/photos/37539977@N00/3203922211/ ORIGINS Illustration from Charles Darwin's “The Expression of the Emotions in Man and Animals”.
    [Show full text]
  • An Emotional Analysis of False Information in Social Media and News Articles
    An Emotional Analysis of False Information in Social Media and News Articles BILAL GHANEM, Universitat Politècnica de València, Spain PAOLO ROSSO, Universitat Politècnica de València, Spain FRANCISCO RANGEL, Universitat Politècnica de València; Autoritas Consulting, Spain Fake news is risky since it has been created to manipulate the readers’ opinions and beliefs. In this work, we compared the language of false news to the real one of real news from an emotional perspective, considering a set of false information types (propaganda, hoax, clickbait, and satire) from social media and online news articles sources. Our experiments showed that false information has different emotional patterns in each of its types, and emotions play a key role in deceiving the reader. Based on that, we proposed a LSTM neural network model that is emotionally-infused to detect false news. CCS Concepts: • Computing methodologies → Neural networks; Natural language processing. Additional Key Words and Phrases: Fake News, Suspicious News, False information, Emotional Analysis ACM Reference Format: Bilal Ghanem, Paolo Rosso, and Francisco Rangel. 2018. An Emotional Analysis of False Information in Social Media and News Articles. 1, 1 (August 2018), 17 pages. https://doi.org/10.1145/1122445.1122456 1 INTRODUCTION With the complicated political and economic situations in many countries, some agendas are publishing suspicious news to affect public opinions regarding specific issues [23]. The spreading of this phenomenon is increasing recently with the large usage of social media and online news sources. Many anonymous accounts in social media platforms start to appear, as well as new online news agencies without presenting a clear identity of the owner.
    [Show full text]
  • COMMUNICATOR for a CYBER AGE in Africa. Edited by Fr
    COMMUNICATOR for a CYBER AGE in Africa. Edited by Fr. K D Tom Kunnel sdb Bosco Eastern Africa Multimedia Services (B.E.A.M.S) Salesians of Don Bosco Karen, 00502 Kenya COMMUNICATOR FOR A CYBER-AGE IN AFRICA TABLE OF CONTENTS Acknowledgement vi Foreword vii Introduction ix Guidelines x SECTION - 1 1.1 Communication and our Human Identity 3 1.2 The Challenge of Communication 7 1.3 Definition, Components and Forms of Communication 12 1.4 Simplified Models of Communication 18 1.5 Noise in Communication 23 1.6 Body Language 27 1.7 Eye Communication 31 1.8 The Language of Signs and Symbols 36 1.9 Etiquette for Better Communication 41 1.10 A Brief History of Communication 49 SECTION - 2 2.1 Intra-Personal Communication 57 2.2 Communication and Self Management 65 2.3 Advanced Models of Communication 72 2.4 Visual Media 77 2.5 Characteristics of an Audio-Visual Culture 87 2.6 A Biblical Framework for Human Communication 92 2.7 Significance of Words According to Scripture 96 2.8 Guidelines for Liturgical Music 100 2.9 Drama in the Liturgy 107 2.10 Dance in the Liturgy 113 SECTION - 3 3.1 Human Person as Communicator 119 3.2 Media and Meaning 123 3.3 The Levels of Meaning in Communication 127 3.4 People, Context, and Communication 131 3.5 Perception, Bias and Stereotypes 136 3.6 What is Truth? 144 3.7 Tolerance and the Communication of Truth 150 3.8 Reconstructing Reality 157 3.9 Dialogue – A Way of Being Fully Human 161 3.10 Enunciation Skills 167 3.11 Public Speaking Skills 173 3.12 The Power of Mass Media 180 3.13 Listening, the Biblical Perspective
    [Show full text]
  • Robert Plutchik's PSYCHOEVOLUTIONARY THEORY of BASIC EMOTIONS
    Robert Plutchik's PSYCHOEVOLUTIONARY THEORY OF BASIC EMOTIONS Postulates 1 ANIMALS AND HUMANS The concept of emotion is applicable to all evolutionary Basic Emotions, Similarity, Intensity levels and applies to all animals as well as humans. 2 EVOLUTIONARY HISTORY Emotions have an evolutionary history and have evolved various forms of expression in different species. 3 SURVIVAL ISSUES acceptance Emotions serve an adaptive role in helping organisms deal with key survival issues posed by the environment. delight tolerance timidity serenity PROTOTYPE PATTERNS trust apprehension 4 cheerfulness Dispite different forms of expression of emotions in different species, there are certain common elements, or prototype patterns, that can be identified. joyjoy fear admiration fright dismay 5 BASIC EMOTIONS elation panic There is a small number of basic, primary or prototype terror emotions. attentiveness extasy uncertainty 6 COMBINATIONS expectancy amazement All other emotions are mixed or derivative states; that is, vigilance surprise they occur as combinations, mixtures, or compounds of anticipation astonishment the primary emotions. curiosity distraction HYPOTHETICAL CONSTRUCTS 7 interest fury grief Primary emotions are hypothetical constructs or idealized states whose properties and characteristics can only be rage inferred from various kinds of evidence. loathing sorrow anger 8 OPPOSITES revulsion sadness Primary emotions can be conceptualized in terms of hostility dejection polar opposites. disgust gloominess 9 SIMILARITY annoyance pensiveness
    [Show full text]
  • Sharing Emotional Information Using a Three Layer Model
    ICIW 2011 : The Sixth International Conference on Internet and Web Applications and Services Sharing Emotional Information Using A Three Layer Model Imen Tayari Meftah1;2 and Nhan Le Thanh1 Chokri Ben Amar2 1University of Nice Sophia Antipolis and CNRS 2University of Sfax and ENIS I3S Laboratory REGIM laboratory Sophia Antipolis, France Sfax, Tunisia Email: [email protected], [email protected] Email: [email protected] Abstract—In this study, we present a generic model to In Section 3, we describe the different parts of our model. exchange emotional states information between heterogeneous Finally, we conclude in Section 4. multi-modal applications. Our proposal is composed of three distinct layers: the psychological layer, the formal computa- tional layer and the language layer. The first layer represents II. PROBLEM STATEMENT the psychological theory adopted in our approach, The second layer is based on a formal multidimensional model. It matches Emotions are an important key component of human with the psychological approach of the previous layer. The social interaction. Today there is a need for computers to un- final layer uses XML to generate the final emotional data to derstand this component in order to facilitate communication be transferred through the network. The remainder of this between users and to improves the credibility of interactions article describes each layer of our model. The proposed model enables the exchange of the emotional states regardless to the human-computer. Sharing emotional states becomes more modalities and sensors used in the detection step. Moreover our and more important in human-machine interactive systems.
    [Show full text]
  • Plutchik's Wheel of Emotions
    Learn about EQ Assessments & Resources Certification The EQ Network Plutchik’s Wheel of Emotions – Search 2017 Update Search by Melissa Donaldson | Apr 27, 2017 | EQ Business, EQ Life | 11 comments Related... Tags: anger, Enhance If you want to enhance your emotional literacy, Robert Plutchik’s wheel of Emotional emotions is a useful tool. Literacy To practice emotional intelligence effectively, you need to have a robust and nuanced emotional vocabulary. Here at Six Seconds, we call it Tags enhancing emotional literacy. It’s the foundation for essential EQ skills Select a To like recognizing, labeling, and navigating emotions. But how can go pic about increasing our emotional vocabulary? Robert Plutchik’s wheel of emotions is a great place to start – and we included an interpretation Recent guide below! posts Best of Q3 Trust Takeaways Expert Panel: Leadership to Improve Trust & Communication Fights Well With Others: Tips for Collaborative Parenting Summit 2017 ACE Project Opportunity The Trust Revolution: 4 Powerful Strategies from Neuroeconomis t Paul Zak How to Be a Great Leader: Harnessing the Power of Noble Goals As explained in Josh Freedman’s bestselling book “At the Heart of Leadership,” Robert Plutchik’s three-dimensional model describes the relations among emotions, which is extremely helpful in understanding how complex emotions interact and change over time. So, what do all those colors and petals mean? Get a free worksheet on the 8 basic emotions and the purpose each serves Breaking Down Plutchik’s Wheel of Emotions Primary: The eight sectors are designed to indicate that there are eight primary emotion dimensions. They are anger, anticipation, joy, trust, fear, surprise, sadness and disgust.
    [Show full text]