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Human perception studies examine the meanings people infer from each emoji, producing an interpretable taxonomy linking emojis to perceived emotions. 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Interactions  \nby  \nGhazal Saheb Jam  \nB.Sc., University of Tehran, 2019  \nThesis Submitted in Partial Fulfillment of the  \nRequirements for the Degree of  \nMaster of Science  \nin the  \nSchool of Computing Science  \nFaculty of Applied Sciences  \n© Ghazal Saheb Jam 2021  \nSIMON FRASER UNIVERSITY  \nFall 2021  \nCopyright in this work is held by the author. Please ensure that any reproduction or re-use is done in accordance with the relevant national copyright legislation.  \nDeclaration of Committee  \nName: Ghazal Saheb Jam  \nDegree: Master of Science  \nThesis title: Developing a Data-Driven Categorical Taxonomy  \nof Emotional Expressions in Real World Human Robot Interactions  \nCommittee: Chair: Mo Chen  \nAssistant Professor, Computing Science  \nAngelica Lim  \nSupervisor  \nAssistant Professor, Computing Science  \nParmit Chilana  \nCommittee Member  \nAssociate Professor, Computing Science  \nPhilippe Pasquier  \nExaminer  \nAssociate Professor, Interactive Arts and Technology  \n􀀂􀀌􀀇􀀈􀀅􀀋 􀀃􀀌􀀄􀀌􀀆􀀉􀀆􀀊􀀌  \nAbstract  \nEmotions are reactions that can be expressed through a variety of social signals [14] [23] . For example, anger can be expressed through a scowl, narrowed eyes, a long stare, or many other expressions. This complexity is problematic when attempting to recognize a human’s expression in a human-robot interaction: categorical emotion models used in HRI typically use only a few prototypical classes, and do not cover the wide array of expressions in the wild. We propose a data-driven method towards increasing the number of known emotion classes present in human-robot interactions, to 28 classes or more. The method includes the use of automatic segmentation of video streams into short (\u003C10s) videos, and annotation using the large set of widely-understood emojis as categories. We then investigate the meaning behind these emojis by studying how humans perceive these emojis. We showcase our results in a taxonomy which includes each emoji and the different meanings people perceived from it. This framework for social signal analysis can be used in the future by researchers to capture what social signals are happening in the wild. Furthermore, researchers can use emojis as social signal representation labels for training machine learning models, towards more accurate human emotion recognition by robots.  \nKeywords: emotion-recognition, social signals, in-the-wild, emotion categories, emojis  \nAcknowledgements  \nDuring the writing of this thesis I have received a great deal of support and assistance. I would like to thank my supervisor, Dr. Angelica Lim, who has provided invaluable insight and guidance. I would like to express my sincere gratitude to Dr. Mo Chen for supporting me throughout my degree. I would like to express my appreciation to Dr. Parmit Chilana and Dr. Philippe Pasquier for accepting to being my committee members and for providing insightful suggestions.  \nI would like to thank Dr. Jimin Rhim for her friendship and advices that she have shared during the project. Also I would like to express my infinite and endless gratitude tomy family for always supporting me in life.  \nTable of Contents  \nDeclaration of Committee ii  \nEthics Statement iii  \nAbstract iv  \nAcknowledgements v  \nTable of Contents vi  \nList of Tables viii  \nList of Figures ix  \n1 Introduction 1  \n1. 1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.2 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2  \n2 Related Work 3  \n2. 1 Emotion models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n2.1.1 Categorical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n2.1.2 Dimensional models . . . . . . . . . . . . . . . . . . . . . . . . . . . 4  \n2.1.3 Hybrid Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5  \n2.1.4 Emotion models used in HRI . . . ","cbCaiq0vmcdtRF90","https://ap.wps.com/l/cbCaiq0vmcdtRF90","pdf",8744743,82,"English","# 1 Introduction\n## 1.1 Contributions\n## 1.2 Thesis Outline\n# 2 Related Work\n## 2.1 Emotion models\n## 2.2 Emotional expressions in videos\n# 3 Data-driven discovery of social signals\n## 3.1 Data preparation method\n## 3.2 Preliminary study: Emoji selection\n## 3.3 Experiment 1: Social signal annotation\n# 4 Social signals to emotions\n## 4.1 Experiment 2: Emoji meanings\n# 5 Social Signal Taxonomy\n## 5.1 Initial taxonomy from experiment 1\n## 5.2 Improved taxonomy from experiment 2\n## 5.3 Alternative representation\n# 6 Conclusion and Future Work\n## 6.1 Limitations\n## 6.2 Future work","[{\"question\":\"Why do existing categorical emotion models struggle in real-world human-robot interactions?\",\"answer\":\"They rely on only a few prototypical classes and cannot cover the wide variety of emotional expressions observed in real-world settings.\"},{\"question\":\"What is the core method proposed to expand known emotion classes?\",\"answer\":\"The approach segments video streams into short clips and annotates them using a large set of widely understood emojis as category labels.\"},{\"question\":\"How are the meanings of emojis incorporated into the final taxonomy?\",\"answer\":\"Humans are studied to determine how they perceive and interpret each emoji, and those perceived meanings are used to build the taxonomy.\"}]","Developing a Data-Driven Categorical Taxonomy of Emotional Expressions in Real World Human Robot Interactions - Thesis Master of Science | PDF",207]