• Science of Emotion and Sensibility
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• v.15 no.2
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• pp.283-296
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• 2012

The goal of this study is to determine the social emotion model as an emotion sharing relationship and information sharing relationship based on the user's relations at social networking services. 26 social emotions were extracted by verification of compliance among 92 different emotions collected from the literature survey. The survey on the 26 emotion words was verified to the similarity of social relation types to the Likert 7-points scale. The principal component analysis of the survey data determined 12 representative social emotions in the emotion sharing relation and 13 representative social emotions in the information sharing relation. Multidimensional scaling developed the two-dimensional social emotion model of emotion sharing relation and of information sharing relation based on online communication environment. Meanwhile, insignificant factors in the suggest social emotion models were removed by the structural equation modeling analysis, statistically. The test result of validity analysis demonstrated the fitness of social emotion models at emotion sharing relationships (CFI: .887, TLI: .885, RMSEA: .094), social emotion model of information sharing relationships (CFI: .917, TLI: .900, RMSEA : 0.050). In conclusion, this study presents two different social emotion models based on two different relation types. The findings of this study will provide not only a reference of evaluating social emotions in designing social networking services but also a direction of improving social emotions.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2373-2378
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• 2005

An emotion detection algorithm using frontal facial image is presented in this paper. The algorithm is composed of three main stages: image processing stage and facial feature extraction stage, and emotion detection stage. In image processing stage, the face region and facial component is extracted by using fuzzy color filter, virtual face model, and histogram analysis method. The features for emotion detection are extracted from facial component in facial feature extraction stage. In emotion detection stage, the fuzzy classifier is adopted to recognize emotion from extracted features. It is shown by experiment results that the proposed algorithm can detect emotion well.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.105-110
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• 2008

Human being recognizes emotion fusing information of the other speech signal, expression, gesture and bio-signal. Computer needs technologies that being recognized as human do using combined information. In this paper, we recognized five emotions (normal, happiness, anger, surprise, sadness) through speech signal and facial image, and we propose to method that fusing into emotion for emotion recognition result is applying to multimodal method. Speech signal and facial image does emotion recognition using Principal Component Analysis (PCA) method. And multimodal is fusing into emotion result applying fuzzy membership function. With our experiments, our average emotion recognition rate was 63% by using speech signals, and was 53.4% by using facial images. That is, we know that speech signal offers a better emotion recognition rate than the facial image. We proposed decision fusion method using S-type membership function to heighten the emotion recognition rate. Result of emotion recognition through proposed method, average recognized rate is 70.4%. We could know that decision fusion method offers a better emotion recognition rate than the facial image or speech signal.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.11a
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• pp.19-20
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• 2011

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2012.05a
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• pp.11-12
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• 2012

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.284-288
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.347-350
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.3_4
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• pp.418-428
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• 2003

The purpose of this study was to provide the guidance in more objective and proper clothing design reflecting today's consumers' modes in value consumption by identifying the meaning structure and relationship between fashion sensibility and emotion. The stimulus was 54 photos of contemporary costume which represented the Izard' DES. The questionnaire consisted of hi-polar 25 pairs adjective scale of fashion sensibility and the 18 noun scale of emotion was distributed to 970 male and female living in Pusan area. The data were analyzed by Factor analysis, Correlation analysis and Regression analysis using the statistical SPSS package. The major finding of this research were as follows.1. Fashion sensibilities consist of estheticism, maturity, character and feminity to represent 57.17% total varlarlce. 2. Emotions consist of negative emotion, distress afraid, arousal, shame and enjoyment to represent 70.84% total variance. 3. For the relation between fashion sensibility and emotion, they showed significant relationship in most of factors.

• Safety and Health at Work
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• v.14 no.3
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• pp.309-316
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• 2023

Background: Job performance is known as an essential reflection of nursing quality. Colleague solidarity, positive emotion, and turnover intention play effective roles in a clinical working environment, but their impacts on job performance are unclear. Investigating the association between nurses' colleague solidarity and job performance may be valuable, both directly and through the mediating roles of positive emotion and turnover intention. Methods: In this cross-sectional study, a total of 324 Chinese nurses were recruited by convenience sampling method from July 2016 to January 2017. Descriptive analysis, Spearman's correlation analysis, and the structural equation model were applied for analysis by SPSS 26.0 and AMOS 24.0. Results: A total of 49.69% of participants were under 30 years old, and 90.12% of participants were female. Colleague solidarity and positive emotion were positively connected with job performance. The results indicated the mediating effects of positive emotion and turnover intention in this relationship, respectively, as well as the chain mediating effect of positive emotion and turnover intention. Conclusions: In conclusion, dynamic and multiple supportive strategies are needed for nurse managers to ameliorate nursing job performance by improving colleague solidarity and positive emotion and decreasing turnover intention based on the job demand-resource model.

• Korean Journal of Child Studies
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• v.25 no.6
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• pp.355-370
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• 2004

This study was to examine the relationship between temperament and children's emotion regulation with negative emotion. Following are the purposes of this research. First, children's temperament and negative emotion, and emotion regulation was found based on children's sex and age difference. Second, if there is a relationship between children's temperament and children's negative emotion and emotion regulation. The subjects of this study were 213 students who were in 8, 10, 12 ages of elementary school in Mokpo. The scales used in this study was Buss & Plomin(1975)'s EAS(Emotionality, Activity, Sociability, Impulsivity) and Brand & Halpern(1998)'s ERACH(Emotion Response and Coping Interview). The data analysis was made by SPSS PC+, and average, two-way ANOVA, Scheffe test, correlation and were employed to test the research questions. As a result of this research a difference in age was found, and a difference in sex was not found. Children's temperament was in positive relation with anger and emotion approach. This research will assist to make the foundation of counseling psychology and developmental psychology.