• Journal of the Korean Society of Costume
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• v.57 no.9
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• pp.149-160
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• 2007

The purpose of this study was to investigate effects of service qualities and consumption emotion on consumer satisfaction of internet fashion shopping malls. Data were obtained from 304 internet fashion shopping mall consumers who have bought fashion products or visited an internet fashion shopping mall. Questionnaires related to service quality, consumption emotion, consumer satisfaction. For analysis of data, exploratory factor analysis, confirmatory factor analysis, path analysis were applied. The results were as follows: 1. The service quality dimensions of internet fashion shopping malls were reliability, merchandise variability, web-design, communication and safety. The consumption emotion dimensions were classified as positive emotion and negative emotion. 2. The service quality of internet fashion shopping malls and the consumption emotion had an effect on consumer satisfaction of internet fashion shopping malls. The dimensions of communication, merchandise variability of the service quality in internet fashion shopping malls had an effect on positive emotion. Safety, reliability of the service quality had an effect on negative emotion. Both positive emotion and negative emotion of the consumption emotion dimensions had an effect on consumer satisfaction of internet fashion shopping malls.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.215-220
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• 2022

Emotion recognition is one of the most important and challenging areas of computer vision. Nowadays, many studies on emotion recognition were conducted and the performance of models is also improving. but, more research is needed on emotion recognition and sentiment analysis of video viewers. In this paper, we propose an emotion analysis system the includes a sentiment analysis model and an interest prediction model. We analyzed the emotional patterns of people watching popular and unpopular videos and predicted the level of interest using the emotion analysis system. Experimental results showed that certain emotions were strongly related to the popularity of videos and the interest prediction model had high accuracy in predicting the level of interest.

• Korean Journal of Childcare and Education
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• v.18 no.4
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• pp.1-16
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• 2022

Objective: The purpose of this study is to test the psychometric properties of the Maternal Emotion Coaching Questionnaire (MECQ, Lim et al., 2018) in order to measure emotion coaching in mothers of preschoolers. Methods: A total of 316 preschoolers and their mothers participated in this study. Maternal emotion coaching was assessed by self-report and child emotion regulation ability was evaluated by the teacher. Data were analyzed with chi-square tests, reliability analysis, confirmatory factor analysis, latent profile analysis, and F-test. Results: Each item of the MECQ showed proper discriminative power. The MECQ and each subscale demonstrated adequate internal consistency and split-half reliability. Evidence of construct validity was provided by confirmatory factor analysis. The five-factor model including maternal attention, awareness, acceptance, empathy, and guidance showed a good fit. Results of the latent profile analysis revealed three profiles of emotion coaching: excellent, good, and poor. Preschoolers with mothers in the poor coaching profile showed significantly lower emotion regulation ability compared to those in the excellent or good coaching profiles, which suggested discriminative validity of the MECQ. Conclusion/Implications: The MECQ presents a reliable and valid tool to assess emotion coaching in mothers of preschool children and can thus be effectively used for mothers of preschoolers.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.940-943
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• 2022

This study aims to analyze the Korean dataset by performing Korean sentence Emotion Analysis in the radio listeners' text messages collected personally. Currently, in Korea, research on the Emotion Analysis of Korean sentences is variously continuing. However, it is difficult to expect high accuracy of Emotion Analysis due to the linguistic characteristics of Korean. In addition, a lot of research has been done on Binary Sentiment Analysis that allows positive/negative classification only, but Multi-class Emotion Analysis that is classified into three or more emotions requires more research. In this regard, it is necessary to consider and analyze the Korean dataset to increase the accuracy of Multi-class Emotion Analysis for Korean. In this paper, we analyzed why Korean Emotion Analysis is difficult in the process of conducting Emotion Analysis through surveys and experiments, proposed a method for creating a dataset that can improve accuracy and can be used as a basis for Emotion Analysis of Korean sentences.

• Journal of Fashion Business
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• v.17 no.2
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• pp.136-150
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• 2013

This study investigates the effect of customer emotions on customer evaluation and behavior intention. The subjects used in this study were customers of a fashion shop in Sunchon South Korea. The questionnaires were conveniently sampled from July 2010 to August, 2010. Questionnaire data from 335 customers of a national brand were analyzed through a reliability analysis, factor analysis, and multiple regression analysis. The results of this study are as follows. First, emotions of customer were divided into 2 patterns, positive emotion and negative emotion. Second positive emotion have significant (+) influences on the trust and negative emotion have significant (-) influences on the trust. Third positive emotion have significant (+) influences on the customer orientation and negative emotion have significant (-) influences on the customer orientation. Forth, the emotions of customer have a considerable impact on the interaction intention. And the positive emotion have significant (+) influences on the word-of-mouth intention and negative emotion have not a considerable impact on it. Fifth the positive emotion have significant (+) influences on the attitude toward store and repurchase intention, and negative emotion have significant (-) influences on the attitude toward store and repurchase intention.

• The Korean Journal of Franchise Management
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• v.12 no.1
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• pp.45-55
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• 2021

Purpose: How to build the positive emotion of customer is very important, because it affects the positive attitude. Brand evidence has a significant impact on consumer behavior in terms of reinforcing consumers' perception of food service companies and differentiating them from competing brands. Thus, this study examines the effect of brand evidence on emotion (positive emotion and negative emotion), and attitude in restaurant industry. Research design, data, and methodology: This study examines the structural relationship among brand evidence, emotion, and attitude. Brand evidence divide into three sub-dimensions such as physical evidence, core service, and employee service. In order to test the purposes of this study, research model and hypotheses were developed. The questionnaire items were modified and used according to the content of this study based on previous studies. All constructs were measured by multiple items tested and developed in the previous research. The data were collected from 439 restaurant users from Seoul area were analyzed using SPSS 22.0 and SmartPLS 3.0 program. A total of 460 questionnaires were distributed and a survey was conducted for 4 weeks, and a total of 439 were used for analysis, excluding non-response data and 21 unusable response data among the collected questionnaires. Frequency analysis was conducted to identify the general characteristics of the survey subjects. To measure the reliability and validity of the measurement tools, confirmatory factor analysis was conducted. Structural model analysis was conducted to verify the research model. Result: The findings demonstrate that physical evidence, core service, employee service had positive effects on positive emotion. And core service and employee service had negative effects on negative emotion while physical evidence did not have. Also, positive emotion had positive effect on attitude and negative emotion had negative effect on attitude. Conclusions: The findings of this study provide guidelines on how to enhance competitiveness in restaurant industry through understanding brand evidence's effects on raising perceived consumer's emotion and attitude. Therefore, food service companies should establish a marketing strategy that can stimulate positive emotions through brand evidence, which is all factors related to service brands that influence consumers' evaluation of service products and purchase decision-making process.

• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.79-85
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• 2017

Understanding of human emotion has a high importance in interaction between human and machine communications systems. The most expressive and valuable way to extract and recognize the human's emotion is by facial expression analysis. This paper presents and implements an automatic extraction and recognition scheme of facial expression and emotion through still image. This method has three main steps to recognize the facial emotion: (1) Detection of facial areas with skin-color method and feature maps, (2) Creation of the Bezier curve on eyemap and mouthmap, and (3) Classification and distinguish the emotion of characteristic with Hausdorff distance. To estimate the performance of the implemented system, we evaluate a success-ratio with emotional face image database, which is commonly used in the field of facial analysis. The experimental result shows average 76.1% of success to classify and distinguish the facial expression and emotion.

• Korean Journal of Childcare and Education
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• v.13 no.3
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• pp.39-54
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• 2017

Objective: The purpose of this study was to examine natural groupings of the sub-factors of mother's reaction to children's negative emotions. The natural groupings were as follows; the emotion-coaching-reaction, the emotion-minimizing-reaction and oversensitive reaction. In addition, this paper also investigated individual differences in children's emotion regulation by clusters of sub-factors of mother's reaction to children's negative emotions. Methods: The subjects of this study consisted of 318 children. The data were analyzed using cluster analysis and one-way ANOVA. Results: The results suggested four proper clusters, according to the characteristics of mother's reaction to children's negative emotions. Cluster 1 was categorized as 'child centered-emotion coaching', cluster 2 was categorized as 'oversensitive-emotion coaching comorbid', cluster 3 was categorized as 'acception-emotion minimizing confused' and cluster 4 was categorized as 'emotion minimizing-unsupporting.' Additionally, the differences between Emotion regulations in each cluster showed distinct points of interest. In terms of the maladaptive emotion regulation, cluster 3 showed the highest level followed by cluster 4. And cluster 1 and 2 showed the lowest level. Conclusion/Implications: The results of this study helped to find a deeper understanding of the operation of specific clusters of mother's reaction to children's negative emotion and children's emotion regulation.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.37-43
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• 2018

In this paper, we propose a framework that automatically suggests emotion using emotion analysis method based on facial expression change. We use Microsoft's Emotion API to calculate and analyze emotion values in facial expressions to recognize emotions that change over time. In this step, we use standard deviations based on peak analysis to measure and classify emotional changes. The difference between the classified emotion and the normal emotion is calculated, and the difference is used to recognize the emotion abnormality. We match user's emotions to relatively relaxed emotions using histograms and emotional meshes. As a result, we provide relaxed emotions to users through images. The proposed framework helps users to recognize emotional changes easily and to train their emotions through emotional relaxation.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.271-279
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• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.