• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.11a
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• pp.21-22
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• 2011

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.389-401
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• 2019

The purpose of this study is to clarify the relationship between servicescape experience of visitors to professional baseball stadium, control perception on the spot, consumption emotion, and revisit intention. A total of 273 questionnaires were analyzed using SPSS 20.0 and AMOS 20.0. The validity of the data was verified through frequency analysis, reliability analysis, confirmatory factor analysis, and correlation analysis. The hypothesis was verified by structural equation model analysis. First, servicescape has a statistically significant effect on control perception. Second, the control perception in the servicescape has a significant effect on the consumption emotion. Third, servicescape effected consumption emotion. Fourth, consumption emotion had a significant influence on the revisit intention. The results of this study suggest that visitors to baseball stadiums can induce revisit intention through positive experience of servicescape. The specific discussions and implications are described in the text.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.73-79
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• 2010

Emotion intelligence computing is able to processing of human emotion through it's studying and adaptation. Also, Be able more efficient to interaction of human and computer. As sight and hearing, music & image is constitute of short time and continue for long. Cause to success marketing, understand-translate of humanity emotion. In this paper, Be design of check system that matched music and image by user emotion keyword(irritability, gloom, calmness, joy). Suggested system is definition by 4 stage situations. Then, Using music & image and emotion ontology to retrieval normalized music & image. Also, A sampling of image peculiarity information and similarity measurement is able to get wanted result. At the same time, Matched on one space through pared correspondence analysis and factor analysis for classify image emotion recognition information. Experimentation findings, Suggest system was show 82.4% matching rate about 4 stage emotion condition.

• Journal of the Korea Safety Management & Science
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• v.17 no.3
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• pp.247-263
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• 2015

This study seeks to identify effects derived from emotional labor in the distribution industry, and draw ways to systematically manage the employees by exploring effects of the emotion work on performance. For the purpose, literature reviews and empirical experiments were conducted to find out effects of emotional labor and false face acting on emotion work and organizational performance and effects of social support and job autonomy on the relationship between emotion work and organizational performance. To verify the hypothesis, we conducted a hierarchical regression analysis and structural equation model analysis using SPSS 20 and AMOS19. The result of the verification in this study is as follows: First, effects of emotional labor on burnout was found to be statistically significant, second, as for the path-coefficient for "emotional labor ${\rightarrow}$ emotion work" and "emotional labor ${\rightarrow}$ job satisfaction" was not statistically significant, while the path-coefficient for "emotional labor ${\rightarrow}$ service level" was found to be statistically significant. Third, effects of emotion work on job satisfaction was found statistically significant, fourth, emotion work on the service level was found statistically significant, fifth, effects of false face acting on emotion work was found statistically significant, sixth, effects of false face acting on burnout was statistically significant, seventh, moderating were found statistically significant and lastly, moderating effects of the relationship between emotion work from job autonomy and organization performance was not verified in job satisfaction, while emotion work, job autonomy, and interaction variable in service level were statistically significant.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.161-170
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• 2017

Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

• Science of Emotion and Sensibility
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• v.5 no.1
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• pp.61-70
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• 2002

The purpose of this study was to develop 'Emotion Rating Scale induced by Odors'and to identify the structure of odor emotion induced by odors. At first 37 adjectives that describe odor to develop a rating scale were selected. Subjects were to rate odor emotion on a 7-point bipolar scale. 304 subjects participated and were as a group instructed to rate odor emotion. 53 out of 304 subjects were retested to test for reliability of the scale two weeks after under the same condition and finally 25 adjectives were then selected based on high test-retest reliability and factor loading, high contributing to one factor. 24 subjects each in 10s, 20s, 30s & 40s were to rate odor emotion induced by 5 different odors on the scale developed. The structure of odor emotion consisted of 'Esthetics', 'Intensity', 'Romance', 'Nature'and 'Character'. The structure of odor emotion by age appeared quite similar but that by different odors was little bit different.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.267-278
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• 2022

This paper was intended to examine the function of emotion that affects the accuracy of statements in the context of eyewitness testimony. The main emotion theories and concepts introduced in previous studies examining the relation between testimony accuracy and negative emotions were examined based on the characteristics of the research method. The results were presented in the order of emotion definition, emotion inducing method, and emotion measurement method. Specifically, the definition of emotion was described based on studies on negative emotions, arousal, stress, and mood. The emotion inducing method was mainly described based on images, virtual reality, and staged events designed by researchers, which have been mainly used in laboratories. Emotion measurement methods were described with respect to the self-report, behavioral checklist, and psychophysiology. In addition, the emotional approach for objective and scientific repeated verification, the importance of effective experimental design and appropriate scientific memory test, and the need for individual difference control were discussed. This paper reinterprets the contradictions shown by previous research by systematically structuring the function of emotion that affects the accuracy of testimony. It was meaningful to provide a frame for comparative analysis of related studies. Ultimately, it is expected that such knowledge will be used as basic documents for judging the reliability of eyewitness testimony in a legal context.

• Science of Emotion and Sensibility
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• v.14 no.1
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• pp.27-38
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• 2011

This study was aimed to investigate the effects of restaurant in-store color and music congruency on consumer's emotional responses and behavioral intentions. The web survey was conducted among 400 customers(aged from 20~39 years old) who lived in Seoul and Kyunggi, Incheon Province. To find ensemble effect of color and music, 3D studio MAX were used to make high-stimulus(exciting) and low-stimulus(calm) and 3D virtual reality restaurant simulation stimulus were applied. The statistical data analyses were performed using SPSS/WIN 18.0 and reliability analysis, factor analysis, regression analysis were used. Based on the result of the conducting factor analysis, emotional responses were classified into 2 factors: positive emotion and negative emotion. Satisfaction was classified into 1 factor: satisfaction. Loyalty was classified into 1 factor: loyalty. Cronbach's alpha was calculated for the reliability of the survey instrument. Consequently, restaurant in-store color and music congruency were shown to affect positive emotion and negative emotion. Positive emotion and negative emotion were shown to affect satisfaction. Satisfaction were shown to affect loyalty. Music congruency had a higher effect on positive emotion than color congruency. Color congruency had a higher effect on negative emotion than music congruency. The results of this study will serve as a basis of color and music congruency with restaurant atmospherics.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.05a
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• pp.27-28
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• 2011