• Smart Media Journal
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• v.9 no.1
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• pp.51-59
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• 2020

From PC communication to the development of the internet, a new term has been coined on the social media, and the social media culture has been formed due to the spread of smart phones, and the newly coined word is becoming a culture. With the advent of social networking sites and smart phones serving as a bridge, the number of data has increased in real time. The use of new words can have many advantages, including the use of short sentences to solve the problems of various letter-limited messengers and reduce data. However, new words do not have a dictionary meaning and there are limitations and degradation of algorithms such as data mining. Therefore, in this paper, the opinion of the document is confirmed by collecting data through web crawling and extracting new words contained within the text data and establishing an emotional classification. The progress of the experiment is divided into three categories. First, a word collected by collecting a new word on the social media is subjected to learned of affirmative and negative. Next, to derive and verify emotional values using standard documents, TF-IDF is used to score noun sensibilities to enter the emotional values of the data. As with the new words, the classified emotional values are applied to verify that the emotions are classified in standard language documents. Finally, a combination of the newly coined words and standard emotional values is used to perform a comparative analysis of the technology of the instrument.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.225-229
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• 1998

The purpose of the current study was comparative analysis of autonomic and electrocortical responses to passive and active touch of the tektites with different subjective emotional preference. Perspective goal of the project is development of a template for classification of tactile stimuli according to subjective comfort and associated physiological manifestations. The study was carried out on 36 female college students. Physiological signals were acquired by Grass and B10PAC 100 systems with AcqKnowledge III software. Frontal, parietal and occipital EEG (relative power spectrum /percents/ of EEG bands - delta, theta, slow and fast alpha, low and fast beta), and autonomic variables, namely heart rate (HR), respiratory sinus arrhythmia (RSA), pulse transit time (PTT), respiration rate (RSP) and skin conductance parameters (SCL, amplitude, rise time and number of SCRs) were analyzed for rest baseline and stimulation conditions. Analysis of the overall pattern of reaction indicated that autonomic response to tactile stimulation was manifested in a form of moderate HR acceleration, RSP increase, RSA decrease (lowered vagal tone), decreased n and increased electrodermal activity (increased SCL, several SCRs) that reflects general sympathetic activation. Parietal EEG effects (on contra-lateral side to stimulated hand) were featured by short-term alpha-blocking, slightly reduced theta and significantly increased delta and enhanced fast beta activity with few variations across stimuli. The main finding of the study was that most and least preferred textures exhibited significant differences in autonomic (HR, RSP, PTT, SCR, and at less extent in RSA and SCL) and electrocortical responses (delta, slow and fast alpha, fast beta relative power). These differences were recorded both in passive and active stimulation modes, thus demonstrating reproducibility of distinction between most and least emotionally preferred tactile stimuli, suggesting influence of psychological factors, such as emotional property of stimulus, on physiological outcome. Nevertheless, development of sufficiently sensitive .and reliable template for classification of emotional responses to tactile stimulation based on physiological response pattern may require more extensive empirical database.

• Journal of East-West Nursing Research
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• v.27 no.2
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• pp.104-113
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• 2021

Purpose: The purpose of this study was to investigate the status of emotional labor of nursing personnel working in comprehensive nursing service ward and to suggest the way service improvement can be achieved. Methods: A total of 28 nurses working in comprehensive nursing service ward were divided into four groups to conduct focus group interview. All interviews were recorded and transcribed after the interview to perform data analysis in the order of data classification, topic categorization, and keyword derivation. Results: The five categories of subjects and relating keywords drawn from the focus group interviews are as follows: 1) Emotional labor experience: suppressing emotions, expressing emotions or actions that are different from reality, 2) Situations of emotional labor: verbal abuse and assault, sexual harassment, personal needs and errands, 3) Responses to emotional labor: responding directly, responding directly, receiving senior's help, using the organizational system, persevering, 4) Problems caused by emotional labor: work exhaustion, job change intention, job stress, 5) Protection plan against emotional labor: manual or education for nurses, education for patients and carers, compensation, tough sanctions though system strengthening. Conclusion: This study shows that although nurses working in comprehensive nursing service ward generally experience high levels of emotional labor, the problem solving of them relies mainly on personal response. Therefore, it is necessary to develop various measures to protect nurses in an organizational level response, thus to improve the comprehensive nursing service system.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.957-962
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• 2010

If we want to recognize the human's emotion via the facial image, first of all, we need to extract the emotional features from the facial image by using a feature extraction algorithm. And we need to classify the emotional status by using pattern classification method. The AAM (Active Appearance Model) is a well-known method that can represent a non-rigid object, such as face, facial expression. The Bayesian Network is a probability based classifier that can represent the probabilistic relationships between a set of facial features. In this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with FACS (Facial Action Coding System) for automatically modeling and extracting the facial emotional features. To recognize the facial emotion, we use the DBNs (Dynamic Bayesian Networks) for modeling and understanding the temporal phases of facial expressions in image sequences. The result of emotion recognition can be used to rehabilitate based on biofeedback for emotional disabled.

• Human Ecology Research
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• v.54 no.6
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• pp.631-642
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• 2016

This study analyzed of the themes (wounding and healing); concerning the Sandplay therapy process applied to children with emotional instability and attention deficit problems. The subjects of the study were 30 elementary school children who agreed to a personal-interview with the author. They all were families of receiving basic livelihood welfare living in the same county. After the preliminary analysis, the main study data collected from subjects was analyzed by variance (t-test), frequency analysis, and correlation analysis using the standard table modified in accordance with Mitchell's classification system. Particular attention was made in the analyses of the characteristics of the subjects as members of low-income families. As the Sandplay therapy proceeded, it became evident that the wounding area showed a low frequency while the healing area featured a high frequency, and confirmed (as in previous studies) that the Sandplay therapy would be effective for children with emotional instability as well as for those with attention deficit problems. The analysis of difference between the types of children with both problems revealed that Sandplay therapy was more effective for children with emotional instability than for those with attention deficit problems. The analysis also revealed that Sandplay therapy was more effective for girls than for boys.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• Korean Journal of Cognitive Science
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• v.22 no.3
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• pp.299-326
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• 2011

The text such as stories, blogs, chat, message and reviews have the overall emotional flow. It can be classified to the text having similar emotional flow if we compare the similarity between texts, and it can be used such as recommendations and opinion collection. In this paper, we extract emotion terms from the text sequentially and analysis emotion terms in the pleasantness-unpleasantness and activation dimension in order to identify the emotional flow of the text. To analyze the 'dominant emotion' which is the overall emotional flow in the text, we add the time dimension as sequential flow of the text, and analyze the emotional flow in three dimensional space: pleasantness-unpleasantness, activation and time. Also, we suggested that a classification method to compute similarity of the emotional flow in the text using the Euclidean distance in three dimensional space. With the proposed method, we analyze the dominant emotion in korean modern short stories and classify them to similar dominant emotion.

• Korean Journal of Child Studies
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• v.28 no.4
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• pp.187-196
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• 2007

This study evaluated attachment representation of school aged children, its relation to classification of family drawings, and their association with children's perceptions about families. The attachment representation of 43 children 6-9 years of age was evaluated by the Manchester Attachment Story Task (Green, Stanley, & Goldwyn, 2003) children's family drawings were classified by Fury's Family Drawing Scales (1996). Results showed that 12 children (28%) were avoidant, 23 (54%) secure, 4 (9%) resistant, and 4 (9%) were disorganized. Classification of childhood attachment representation showed a high concordance rate (86%) with family drawings. Securely Attached children showed positive perceptions such as family pride/happiness and vitality/creativity while Insecure children showed negative perceptions like emotional distance, tension and bizarreness.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1364-1369
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• 2018

Speech emotion recognition, which aims to classify speaker's emotional states through speech signals, is one of the essential tasks for making Human-machine interaction (HMI) more natural and realistic. Voice expressions are one of the main information channels in interpersonal communication. However, existing speech emotion recognition technology has not achieved satisfactory performances, probably because of the lack of effective emotion-related features. This paper provides a survey on various features used for speech emotional recognition and discusses which features or which combinations of the features are valuable and meaningful for the emotional recognition classification. The main aim of this paper is to discuss and compare various approaches used for feature extraction and to propose a basis for extracting useful features in order to improve SER performance.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.611-623
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• 2016

This research used one of the qualitative research techniques, in-depth interview method, on Taekwondo Gym instructors in their 30s to 50s with over 10 years experiences in various age groups, to elicit the empirical cases of emotional labor and exhaustion from their work. It categorized the cases into 3 main subjects that identified 2 areas to emphasized in each subjects through the phenomenological classification method. First subject listed, "recognition of emotional labor of the instructors", highlighted "recognition as a manager" and "recognition as an educator". Second subject listed. "meaning and need of emotional labor at the Taekwondo Gym", highlighted "emotional labor as a business strategy" and "emotional labor for educational effects". Third subject listed, "exhaustion from work experienced by the instructors", highlighted "a sense of shame experienced in the field" and "directions for change".