• Journal of Digital Convergence
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• v.15 no.10
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• pp.525-535
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• 2017

The purpose of this study aims to examine consumer perception and current status of water park. The Naver and Daum were used for data collection channels and the keyword 'water park' was used for data retrieval. The data analysis period was limited to the study period from January 1, 2015 to December 31, 2016 for a total of two years. First, as a result of the frequency analysis, hidden cameras, Lotte water park, arrests, suspects, gimhae were in top 5 in 2015, Lotte water park, swimming, summer, opening, admission ticket were in top 5 in 2016. Second, as a result of the connection degree central analysis, hidden camera, arrest, suspect, female, shower room were in top 5 in 2015, swimming, Lotte water park, summer and One Mount, admission ticket were in top 5 in 2016. Third, as a result of the N-GRAM network graph, the water park/hidden camera, the hidden camera/hidden camera, the suspect/arrest, the Gimhae/Lotte water park, water park/suspect were in top 5 in 2015, and One Mount/water park, Gimhae/Lotte water park, water park/admission ticket, water park/water park, water park/opening were in top 5 in 2016. Fourth, as a result of the CONCOR analysis, three groups in 2015 and two groups in 2016 were formed.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.395-404
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• 2015

This study is exploring objective awareness of literary therapy by consideration of popular perception about literary therapy through analysis of big data. The purpose of this study is the deduction of meaning information through analysis in the viewpoint of big data at online social network service(SNS) about 'literary therapy'. Accordingly, the main way of research became content analysis of keyword linked to literary therapy by utilizing opinion mining method related to text mining. The study mainly grasped 'literary therapy' and analyzed 'bibliotherapy' comparatively. The period of study was from Oct. 10th to Nov. 10th, 2014(during 30 days), and SNS such as blog or twitter became the subject of search. Through the result of study analysis, the conclusion that the spread of literary therapeutic prospect, structural harmony of literary therapeutic field, and the solidity of perceptional axis about literary therapy are needed can be drawn. This study is worthwhile because it can investigate popular awareness about literary therapy and can suggest alternative for invigoration of literary therapy.

• Korean Journal of Adult Nursing
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• v.12 no.4
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• pp.690-705
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• 2000

This ethnography was based on Kleinman's explanatory model of a health care system. It is conducted to make thick discription of illness conception of the elderly in a sociocultural context. The basic assumptions were as follows. 1) A health care system is a cultural system, and as with any other cultural system, it is a system of symbolic meanings anchored in a particular arrangement of social institutions and patterns of interpersonal relationships; 2) In all societies health care activities are more or less interrelated. Therefore, they need to be in a holistic manner as socially organized responses to disease that constitute a special cultural system; health care system; 3) Health and illness experiences are the natural process of disease. Individuals who recognized a for state of health, their family, neighbors, and communities define the state, search for causes of the health problems, and response to it. According by, they proceed to search for healing stratagies. So, understanding of the illness experience is the starting point for health care. The study participants were 12 elders aged 60 or more. The fieldwork was conducted in an agricultural clan village of Namwon city. The data collection and analysis were cyclic, from descriptive observation, domain analysis, focused observation, taxanomic analysis, selected observation, componential analysis, and finally cultural themes were all analysed. Proxemic and text analysis techniques were used according to the characteristics of the data. The data of sociocultural context and descriptive data were collected from 1990 to 1992. Informations on illness concepts were collected during 1994 using focused observation. Data confirming and contrast observations were conducted from 1997 and 1999. Illness concepts of the elderly were taxonomized supernatural cause, non-supernatural cause, immediate cause, and ultimate cause. The supernatural ones were ancestors, god of home, god of village, and ghost such as 'sal(evil force of dead man)' and 'gagqui(ghost of begger)'. The non-supernatural ones were Ki, natural phenomenones, natural objects, foods, human and human behaviors. Immediate ones were insufficiency and overflows, discretion and consolidation, disorder and out of order, cloudness and contamination, and fluctuation and stagnation of supernatural cause and non-supernatural ones. Ultimate causes were intrusion and loss of supernatural and nonsupernatural ones. The cultural themes of illness concepts of the elderly are: 1) illness concepts are not based on causality principle, but on reciprocal principle; 2) illness concepts are affected by social level and charicteristics of the patients; 3) the causes of disease are recognized as imposed both positive and negative effects on health based on interpretation of the indiviuals; 4) illness concepts reflects on principles of everyday life of the society members such as hierachial structure and group cohesiveness; 5) illness concepts are ruled on principle of reciprocity and spread; 6) illness concepts are interrelated with physical environment of the participants. It can be concluded that the illness concepts of the elderly in a traditional clan village are a component of health care system as a cultural system based on these results. The these results can be a useful basis for gerontological nursing practice and education.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.383-390
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• 2007

Interactions between proteins and genes are often considered essential in the description of biomolecular phenomena and networks of interactions are considered as an entre for a Systems Biology approach. Recently, many works try to extract information by analyzing biomolecular text using natural language processing technology. Previous researches insist that linguistic information is useful to improve the performance in detecting gene interactions. However, previous systems do not show reasonable performance because of low recall. To improve recall without sacrificing precision, this paper proposes a new method for detection of gene interactions based on syntactic relations. Without biomolecular knowledge, our method shows reasonable performance using only small size of training data. Using the format of LLL05(ICML05 Workshop on Learning Language in Logic) data we detect the agent gene and its target gene that interact with each other. In the 1st phase, we detect encapsulation types for each agent and target candidate. In the 2nd phase, we construct verb lists that indicate the interaction information between two genes. In the last phase, to detect which of two genes is an agent or a target, we learn direction information. In the experimental results using LLL05 data, our proposed method showed F-measure of 88% for training data, and 70.4% for test data. This performance significantly outperformed previous methods. We also describe the contribution rate of each phase to the performance, and demonstrate that the first phase contributes to the improvement of recall and the second and last phases contribute to the improvement of precision.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.141-155
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• 2017

As the use of SNS becomes more active, many people are posting their thoughts about specific events in their SNS in the form of text. As a result, SNS is used in various fields such as finance and distribution to conduct service satisfaction surveys and consumer monitoring. However, in the transportation area, there are not enough cases to utilize unstructured data analysis such as emotional analysis. In this study, we developed an emotional analysis methodology that can be used in transportation by using highway VOC data, which is atypical data collected by Korea Expressway Corporation. The developed methodology consists of morpheme analysis, emotional dictionary construction, and emotional discrimination of the collected unstructured data. The developed methodology was verified using highway related tweet data. As a result of the analysis, it can be guessed that many information and information about the construction and the accident were related to the highway during the analysis period. Also, it seems that users complain about the delay caused by construction and accident.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.423-432
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• 2019

Along with an increase in marine activities every year, the frequency of marine accidents is on the rise. Accordingly, various research activities and policies for marine safety are being implemented. Despite these efforts, the number of accidents are increasing every year, bringing their effectiveness into question. Preliminary studies relying on annual statistical reports provide precautionary measures for items that stand out significantly, through the comparison of statistical provision items. Since the 2000s, large-scale marine accidents have repeatedly occurred, and case studies have examined the "accident response." Likewise, annual statistics or accident cases are used as core data in policy formulation for domestic maritime safety. However, they are just a summary of post-accident results. In this study, limitations of current marine research and policy are evaluated through a literature review of case studies and analyses of marine accidents. In addition, the ontology of the marine accident information classification system will be revised to improve the current limited usage of the information through an attribute analysis of boating accident status reports and text mining. These aspects consist of the reporter, the report method, the rescue organization, corrective measures, vulnerability of response, payloads, cause of oil spill, damage pattern, and the result of an accident response. These can be used consistently in the future as classified standard terms to collect and utilize information more efficiently. Moreover, the research proposes a data collection and quality assurance method for the practical use of ontology. A clear understanding of the problems presently faced in marine safety will allow "suf icient quality information" to be leveraged for the purpose of conducting various researches and realizing effective policies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.548-557
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• 2021

The purpose of this study was to determine the statistical usefulness of using atypical text data collected from media that are easy to collect to overcoming limits of the existing data related to economic activities of disabled people. In addition, by performing semantic network analysis, major issues by period that could not be grasped by statistical analysis were also identified. As a result, semantic network analysis revealed that the initiative of the public sector, such as the central and local government bodies, was strongly shown. On the other hand, in the private purchase sector, it was also possible to confirm the consumption revitalization trend and changes in production activities in the recent issue of Covid-19. While the term "priority purchase" had a statistically significant relation with the other two terms "vocational rehabilitation" and "employment for the disabled". For the regression results, while the term "priority purchase" had a statistically significant association with the other two terms "vocational rehabilitation" and "employment for the disabled". Further, some statistical analyses reveal that keyword data taken from media channels can serve as an alternative indicator. Implications for issue detection in the field of welfare economy for the disabled is also discussed.

• Journal of Korean Dental Hygiene Science
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• v.6 no.1
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• pp.13-25
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• 2023

Background: This study was conducted to confirm the service quality management of care workers, who are direct service personnel of long-term care insurance for the elderly, using unstructured big data. Methods: Using a textome, this study collected and analyzed unstructured social data related to care workers' service quality. Frequency, TF-IDF, centrality, semantic network, and CONCOR analyses were conducted on the top 50 keywords collected by crawling the data. Results: As a result of frequency analysis, the top-ranked keywords were 'Long-term care services,' 'Care workers,' 'Quality of care services,' 'Long term care,' 'Long term care facilities,' 'Enhancement,' 'Elderly,' 'Treatment,' 'Improvement,' and 'Necessity.' The results of degree centrality and eigenvector centrality were almost the same as those of the frequency analysis. As a result of the CONCOR analysis, it was found that the improvement in the quality of long-term care services, the operation of the long-term care services, the long-term care services system, and the perception of the psychological aspects of the care workers were of high concern. Conclusion: This study contributes to setting various directions for improving the service quality of care workers by presenting perceptions related to the service quality of care workers as a meaningful group.

• Smart Media Journal
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• v.12 no.5
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• pp.36-45
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• 2023

Recently, in digital agriculture, the types and utilization of greenhouses based on IoT are spreading, and greenhouses are being modernized, enlarged, and even factoryized using smart technology. However, a specific standardization plan has not been proposed according to the equipment for data collection in the smart greenhouse and the size or shape of the greenhouse. In other words, there is a lack of standard data for facility equipment, such as the type and number of sensors and equipment according to the size of the greenhouse, the type of greenhouse construction film and materials suitable for crops and carbon neutrality. Therefore, in this study, the suitability of the implementation, installation and quantity of IoT equipment for data collection was tested, and some standard technologies were presented through the implementation of data collection and communication methods. In addition, impact strength, tensile, tear, elongation, light transmittance, and lifespan issues for PE, PVC, and EVA, which account for about 90% of existing greenhouses, were presented, and the shape, size, and environmental problems of greenhouses made of films were presented. presented in the text. In this research paper, a standardized carbon-neutral modular smart greenhouse using nano-material film was implemented as a solution to environmental problems such as greenhouse size, farm crop type, greenhouse lifespan, and film, and its performance with existing greenhouses was analyzed and presented. Through this, we propose a modularized greenhouse that can be expanded or reduced freely without distinction in the size of the greenhouse or the shape of farmhouse crops, and the lifespan is extended and standardized. Finally, the average characteristics of greenhouses using existing PE, PVC, and EVA films and the characteristics of greenhouses using new carbon-neutral nanomaterials are compared and reviewed, and a plan to implement an expandable IoT greenhouse that supports carbon neutrality is proposed.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.123-132
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• 2023

Git's commit messages manage the history of source changes during project progress or operation. By utilizing this historical data, project risks and project status can be identified, thereby reducing costs and improving time efficiency. A lot of research related to this is in progress, and among these research areas, there is research that classifies commit messages as a type of software maintenance. Among published studies, the maximum classification accuracy is reported to be 95%. In this paper, we began research with the purpose of utilizing solutions using the commit classification model, and conducted research to remove the limitation that the model with the highest accuracy among existing studies can only be applied to programs written in the JAVA language. To this end, we designed and implemented an additional step to standardize source change data into natural language using GPT. This text explains the process of extracting commit messages and source change data from Git, standardizing the source change data with GPT, and the learning process using the DistilBERT model. As a result of verification, an accuracy of 91% was measured. The proposed model was implemented and verified to ensure accuracy and to be able to classify without being dependent on a specific program. In the future, we plan to study a classification model using Bard and a management tool model helpful to the project using the proposed classification model.