• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1045-1053
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• 2015

In this research, we conducted meta analysis of the cases of "burnout" of social workers from the 1990s to 2013. The result of analysis shows that the factors that cause burnout of social workers include work experience, compensation from work environment, supervision, promotion, work freedom-related factors and client-related factors such as the seriousness of client problems, the appropriateness of client counts, and all factors excluding client-related factors showed signficaint correlatoins. Moreover, the higher the age and the length of work experience, the lower the sense of self-wroth and emotional intelligence; the higher the social support, the lower the possibility of burn out. The research results suggest need for programs to prevent burn out of social service providers both in environment internal and external to organizatoins.

• Database Research
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• v.34 no.3
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• pp.3-13
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• 2018

Text classification is one of the text mining technologies that classifies a given textual document into its appropriate categories and is used in various fields such as spam email detection, news classification, question answering, emotional analysis, and chat bot. In general, the text classification system utilizes machine learning algorithms, and among a number of algorithms, naïve Bayes and support vector machine, which are suitable for text data, are known to have reasonable performance. Recently, with the development of deep learning technology, several researches on applying deep neural networks such as recurrent neural networks (RNN) and convolutional neural networks (CNN) have been introduced to improve the performance of text classification system. However, the current text classification techniques have not yet reached the perfect level of text classification. This paper focuses on the fact that the text data is expressed as a vector only with the word dimensions, which impairs the semantic information inherent in the text, and proposes a neural network architecture based upon the semantic tensor space model.

• Journal of The Korean Society of Integrative Medicine
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• v.8 no.4
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• pp.231-239
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• 2020

Purpose: The purpose of this study is to identify the relationship of between social support, emotional intelligence, depression, and health promotion behaviors of nursing college students, and to establish basic data for the development of a nursing intervention program for health promotion behaviors. Methods: This descriptive correlation study examined the correlation between the social support, emotional intelligence, depression, and health promotion behaviors of nursing students. 203 nursing college students located in J city participated in the study from November to December 2019. The collected data was analyzed used the SPSS WIN 22.0 program. The general characteristics of the subjects were analyzed by frequency and percentage, and health promoting behavior, social support, emotional intelligence, and depression were analyzed using mean and standard deviation. In this study, the correlation between the subjects' social support, emotional intelligence, depression, and health promotion behaviors was analyzed using Pearson correlation coefficient. Results: The study results showed that the subjects' health promotion behaviors averaged 2.22±0.38 points out of 4d social support averaged 3.83±0.59 points out of 5, emotional intelligence averaged 4.53±0.73 out of 7, and depression averaged 0.49±0.42 points out of 2 points. The analysis results of correlation between the subject's health promotion behaviors, social support, emotional intelligence, and depression showed that health promotion behaviors and social support (r=.287, p<.001), health promotion behaviors and emotional intelligence (r=.450, p<.001), and social support and emotional intelligence (r=.450, p<.001) had a positive correlation, but depression and health promotion behaviors (r=-.453, p<.001), depression and social support (r=-.259, p<.001), and depression and emotional intelligence (r=-.322, p<.001) had a negative correlation. Conclusion: This study will provide the basic data for a follow-up researches on the social support, emotional intelligence, depression and health promotion behaviors of nursing college students. It is expected to serve as the basic data for developing nursing intervention programs for health promotion behaviors in the future.

• Asia Marketing Journal
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• v.9 no.3
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• pp.75-115
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• 2007

This study investigated the effect of service provider's recovery effort and pre-failure customer-employee rapport on post-recovery consumer response such as satisfaction, purchase intention, and positive Word-of-Mouth communication. First, this study explored the interaction effect of recovery effort and customer-employee rapport on post-recovery consumer response. The result shows when the level of pre-failure customer-employee rapport is high, customer's positive responses decreased slightly even though they perceived low recovery effort. However, when the level of pre-failure customer-employee rapport is low, customer's responses were decreased considerably in case of low recovery effort. Second, this study examined 'service recovery paradox' which is post-recovery consumer's satisfaction is greater than the case of no service failure. The result shows recovery paradox was not supported in all samples regardless of the level of recovery effort and customer-employee rapport. Synthetically, customer-employee rapport took a buffering role in customer response after service failure although it's not the same as error-free state.

• Journal of Digital Convergence
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• v.11 no.8
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• pp.65-73
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• 2013

Outcome of election depends on which candidate of politics uses more original and creative idea for Ads and PR of election in election campaign strategy of political campaign. Especially, since political Ads and PR are the ways of capturing voters' sensitivities with one line of copy(slogan) and one image, Ads and PR are very important. This research analyzes unique and creative trend of political campaigns which are used in each unit election which is held four times(2010. 6 2 local election, 2011. 10 26 by-election, 2012. 4 11 general election, 2012. 12 19 presidential election) during 2010~2012. For analysis, search analysis of text and image used in video, internet, booklet type of Ads and PR material for election, and election campaign. Video is used in election campaign during election period. Unique and creative political campaign is customized micro-marketing election strategy trend which tries to fit for tendency of backing including gender, age group, social atmosphere, etc. This research excludes the degree of success of this election strategy from subject of analysis.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.277-284
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• 2020

Text mining is a representative method of big data analysis that extracts meaningful information from unstructured and large amounts of text data. Social media such as Twitter generates hundreds of thousands of data per second and acts as a one-person media that instantly and directly expresses public opinions and ideas. The traditional media are delivering informations, criticizing society, and forming public opinions. For this, we compare the responses of SNS with the responses of media on the issue of the termination of the Korea-Japan GSOMIA (General Security of Military Information Agreement), one of the domestic issues in the second half of 2019. Data collected from 201,728 tweets and 20,698 newspaper articles were analyzed by sentiment analysis, association keyword analysis, and cluster analysis. As a result, SNS tends to respond positively to this issue, and the media tends to react negatively. In association keyword analysis, SNS shows positive views on domestic issues such as "destruction, decision, we," while the media shows negative views on external issues such as "disappointment, regret, concern". SNS is faster and more powerful than media when studying or creating social trends and opinions, rather than the function of information delivery. This can complement the role of the media that reflects public perception.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.6
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• pp.119-133
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• 2021

Research has been conducted in many fields to identify research trends using text mining, but in the field of emotional labor, no research has been conducted using text mining to identify research trends. This study uses text mining to deeply analyze 1,465 papers at the Korea Citation Index (KCI) from 2004 to 2019 containing the subject word 'emotional labor' to understand the trend of emotional labor researches. Topics were extracted by LDA analysis, and IDM analysis was performed to confirm the proportion and similarity of the topics. Through these methods, an integrated analysis of topics was conducted considering the usefulness of topics with high similarity. The research topics are divided into 11 categories in descending order: stress of emotional labor (12.2%), emotional labor and social support (12.0%), customer service workers' emotional labor (10.9%), emotional labor and resilience (10.2%), emotional labor strategy (9.2%), call center counselor's emotional labor (9.1%), results of emotional labor (9.0%), emotional labor and job exhaustion (7.9%), emotional intelligence (7.1%), preliminary care service workers' emotional labor (6.6%), emotional labor and organizational culture (5.9%). Through topic modeling and trend analysis, the research trend of emotional labor and the academic progress are analyzed to present the direction of emotional labor research, and it is expected that a practical strategy for emotional labor can be established.

• Science of Emotion and Sensibility
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• v.5 no.4
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• pp.59-66
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• 2002

This study was proposed as an exploratory study for understanding the biological bases and structures of three personality models: Eysenck's PEN model, Gray's BIS/BAS model, and Costa & McCrae's Five Factor Model, which was chosen as the major descriptive model regardless of its biological bases. Besides, Eysenck's impulsivity scale, IVE, was added to demonstrate the relationship of P and impulsivity. Concerning personality, most previous reports have explored the relationships between P300 and the introversion-extraversion of Eysenck's theory because of its putative biological bases. In the present study, forty-eight undergraduate took four personality batteries (ERQ-R, NEO-Pl-R, BIS/BAS, and IVE). Two types of oddball tasks including different stimulus duration were used to induce ERPs (50ms for task 1, 300ms for task 2). Distributional topographies of correlation coefficients with personality traits and ERP components were drawn, and considered for the consistent interpretation of the personality model structures. Even though all equivalences for extraversion of personality batteries showed similarities for their intra-correlation, their correlations with P3 amplitudes were dissociate. Eysenck's E might not be the proper psychometric measure for elucidating its biological bases. The present study supported the negative relationship of P3 amplitude and extraversion, which is the consensus of previous studies. Neuroticism and Psychoticism showed correlations with the earlier sensory processing components such as N1 and P2. This result might explain the reason why most of studies have failed to find biological connections relating them. Interaction between gender and personality traits should be considered for the interpretation of correlations. Two types of auditory stimulus duration had different sensitivity to personality traits.