• Journal of Korea Game Society
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• v.7 no.4
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• pp.33-42
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• 2007

Advanced technologies and techniques make it possible to express digital animation with higher quality. Characters in the virtual animation space are playing an important role in emphasizing the human audiences or interacting directly with them. The movement of characters gives them vital power, and shows their intentions and emotions. To analyze the emotion of character movements, the study develops basic movement sources based on Laban's property elements of movement such as time, space, and flow, and tries to find the relationship between their movements and the emotions they arouse by positioning them on Plutchik's emotional circle. We find that each element of 9 movements represent its own emotion consistently, and has influence on the intensity of emotions clearly.

• Smart Media Journal
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• v.9 no.3
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• pp.90-97
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• 2020

With the development of the Internet and smartphones, digital sound sources are easily accessible, and accordingly, interest in music search and recommendation is increasing. As a method of recommending music, research using melodies such as pitch, tempo, and beat to classify genres or emotions is being conducted. However, since lyrics are becoming one of the means of expressing human emotions in music, the role of the lyrics is increasing, so a study of emotion classification based on lyrics is needed. Therefore, in this thesis, we analyze the emotions of the farewell lyrics in order to subdivide the farewell emotions based on the lyrics. After constructing an emotion dictionary by vectoriziong the similarity between words appearing in the parting lyrics through Word2Vec learning, we propose a method of classifying parting lyrics emotions using Word2Vec and LSTM, which classify lyrics by similar emotions by learning lyrics using LSTM.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.1-11
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• 2021

The users of a chatbot messenger can be better engaged in the conversation if they feel intimacy with the chatbot. This can be achieved by the chatbot's effective expressions of human emotions to chatbot users. Thus motivated, this study aims to identify the appropriate emotional expressions of a chatbot that make people feel the social presence of the chatbot. In the background research, we obtained that facial expression is the most effective way of emotions and movement is important for relationship emersion. In a survey, we prepared moving text, moving gestures, and still emoticon that represent five emotions such as happiness, sadness, surprise, fear, and anger. Then, we asked the best way for them to feel social presence with a chatbot in each emotion. We found that, for an arousal and pleasant emotion such as 'happiness', people prefer moving gesture and text most while for unpleasant emotions such as 'sadness' and 'anger', people prefer emoticons. Lastly, for the neutral emotions such as 'surprise' and 'fear', people tend to select moving text that delivers clear meaning. We expect that this results of the study are useful for developing emotional chatbots that enable more effective conversations with users.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.197-203
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• 2005

In this paper, we propose an emotion recognition using facial Images to effectively design human interface. Facial database consists of six basic human emotions including happiness, sadness, anger, surprise, fear and dislike which have been known as common emotions regardless of nation and culture. Emotion recognition for the facial images is performed after applying the discrete wavelet. Here, the feature vectors are extracted from the PCA and LDA. Experimental results show that human emotions such as happiness, sadness, and anger has better performance than surprise, fear and dislike. Expecially, Japanese shows lower performance for the dislike emotion. Generally, the recognition rates for Korean have higher values than Japanese cases.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.111-121
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• 2020

The purpose of this study is to examine the satisfaction of consumers using the mobile navigation service which is continuously growing. This study examined variables that affect the service use behaviors and satisfaction of 1200 consumers using service. According to the results of the study, consumers used the service 1-2 or 3-4 times per week, more frequently used in the lunch time(weekends) and in the morning(weekdays). The core functional dimension was shown to have the largest effect on satisfaction with the service. Among consumption emotions, positive emotions had effects in the positive direction while negative emotions had effects in the negative direction, and the effect of innovativeness was not significant. Through the foregoing, the use behavior and the formation of satisfaction could be analyzed and the results of this study can be used in establishing service strategies later and can be also used as basic data of the navigation industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.9-19
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• 2012

Virtual human used as HCI in digital contents expresses his various emotions across modalities like facial expression and body posture. However, few studies considered combinations of such nonverbal multimodal in emotion perception. Computational engine models have to consider how a combination of nonverbal modal like facial expression and body posture will be perceived by users to implement emotional virtual human, This paper proposes the impacts of nonverbal multimodal in design of emotion expressed virtual human. First, the relative impacts are analysed between different modals by exploring emotion recognition of modalities for virtual human. Then, experiment evaluates the contribution of the facial and postural congruent expressions to recognize basic emotion categories, as well as the valence and activation dimensions. Measurements are carried out to the impact of incongruent expressions of multimodal on the recognition of superposed emotions which are known to be frequent in everyday life. Experimental results show that the congruence of facial and postural expression of virtual human facilitates perception of emotion categories and categorical recognition is influenced by the facial expression modality, furthermore, postural modality are preferred to establish a judgement about level of activation dimension. These results will be used to implementation of animation engine system and behavior syncronization for emotion expressed virtual human.

• Science of Emotion and Sensibility
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• v.11 no.4
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• pp.637-644
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• 2008

Human emotion has been tried to be recognized by physiological measurements in developing emotion machine enabling to understand and react to user's emotion. This study is to find the time-dependent physiological measurements and their variation characteristics for discriminating emotions according to dimensional emotion model. Ten university students were asked to watch sixteen prepared images to evoke different emotions. Their subjective emotions and autonomic nervous responses such as ECG (electrocardiogram), PPG (photoplethysmogram), GSR (Galvanic skin response), RSP (respiration), and SKT(skin temperature) were measured during experiment. And these responses were analyzed into HR(Heart Rate), Respiration Rate, GSR amplitude average, SKT amplitude average, PPG amplitude, and PTT(Pulse Transition Time). TDPs(Time dependent parameters) defined as the delay, the activation, the half recovery and the full recovery of respective physiological signal in this study have been determined and statistically compared between variations from different emotions. The significant tendencies in TDP were shown between emotions. Therefore, TDP may provide useful measurements with emotion recognition.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2007.05a
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• pp.176-179
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• 2007

Every individual uses, appraises and interacts with products on a daily basis. It is evident from current trends and theories associated with product design, that these products that are a part of our everyday lives, satisfy our needs in two ways, functionally and psychologically. While a product's usability or the service it provides may satisfy our functional needs, it is often the case that when given a choice, we may select one product from the other, even though they are functionally alike. Why do we make these choices, and more importantly, how? When users are satisfied with a product's performance, they seek a stronger emotional involvement with them(Lewalski, 1988). Are emotions responsible for our choices, likes and dislikes of products? What is the nature of this emotional involvement, what are these emotions, and how, if possible, can we design to generate specific emotions? This research proposes to develop into these questions. It is an effort to formulate the underpinnings of "design for emotions" and uncover the possibilities of a design process that places "emotion" as an equally important concern for the design of objects, as functionality or aesthetic appeal. The literature review will include a systematic study of human and product attributes, theoretical and empirical studies of emotion, and the interaction of humans and products is discussed. This project examines what product characteristics lead to an emotional experience, when people interact with them, and suggests a methodology or design guidelines that may allow designers to enhance or specifically modify the emotions experienced by people, while using the products that are a part of their everyday lives.

• Journal of the Daesoon Academy of Sciences
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• v.42
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• pp.201-226
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• 2022

Character education begins with a clear definition of character. There are claims that humanity is reducible to human nature or personality, but when approached from an educational perspective, human nature soon boils down to the question of its ultimate goodness or wickedness. There is a significant difference between the educational systems that emerge from the standpoint that human nature is evil versus the standpoint that human nature is good. With regards to educational outcomes, this can be observed both in terms of human nature and personality. Modern education today sees education as leading the immature to a mature state from the standpoint that human nature is evil. But if human nature is evil, how could we implement an education that would render it good? If character education becomes a system of etiquette education or one of instilling manners whereby simple wrong cases are righted, it would be nothing but a follow-up to the wrong educational cases that had been carried out previously. In that sense, character education is correction; not education. Education should be done in a way that realizes and understands the perfect self rather than unfolding as a process of constantly correcting and reinforcing immature human beings. In that sense, this paper posits that enabling students to understand their own emotions would serve as a correct form of character education. This would be a system of focusing on emotions that reveal the goodness of human nature. Personality can be educated, but education at this time should be a way to bring out a student's already good and even perfected nature. This is more realistic than replacing a 'faulty' character with a good character which supposedly did not exist previously. If personality education morphs into 'emotional self-understanding,' contemplations on 'why not to do' unsavory acts rather than mere negative commands 'don't do that,' and listening to what one's emotions intuit prior to and after given actions, then that would arise to the true standard of a good education.