• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.59-65
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• 2019

This study examines how cinematic texts are used in movies through Lee Jun-ik's 2015 film, and what narrative and visual effects are obtained through them. This film portrays poet Yun Dong-ju as a central figure and chooses to reconstruct his life. The movie, , used Yun's poetry as a device to maximize the lyricism of the film and to suggest a change in the fate of the character and the inside. In other words, uses Yun Dong-ju's poetry to aesthetically express the inner change of the characters in the film and the sensitivity of the film. Through this, I visualize Yun Dong-ju as poet Yun Dong-ju, a poet who was stuffed in literary books, as a normal literary youth. It is also a reminder of the weight of the reality that the present youth is experiencing and the problem of an individual living in history. In this respect, the movie is a major text that depicts the poetry and poetry of the time, and the age of poet through the media. 'Poetry' as the text of the text delivered with the image maximized the lyricism of the image and led to high aesthetic achievement. Through poetry and poetry, it can be regarded as the main text approaching the problems of history, individual, literature and reality.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.203-209
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• 2021

In this study, a prototype design model was proposed for developing an online recruitment system through multi-dimensional data crawling and social media analysis, and validates text information and video interview in job application process. This study includes a comparative analysis process through text mining to verify the authenticity of job application paperwork and to effectively hire and allocate workers based on the potential job capability. Based on the prototype system, we conducted performance tests and analyzed the result for key performance indicators such as text mining accuracy and interview STT(speech to text) function recognition rate. If commercialized based on design specifications and prototype development results derived from this study, it may be expected to be utilized as the intelligent online recruitment system technology required in the public and private recruitment markets in the future.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.13-24
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• 2022

In order to explore the context of fraudulent claims and the measures for preventing them targeting the long-term care institutions for the elderly, which is increasing every year in Korea, this study conducted the text mining analysis using the media report articles. The media report articles were collected from the news big data analysis system called 'BIG KINDS' for about 15 years from July 2008 when the Long-Term Care Insurance for the Elderly took effect, to February 28^th 2022. During this period of time, total 2,627 articles were collected under keywords like 'elderly care+fraudulent claims' and 'long-term care+fraudulent claims', and among them, total 946 articles were selected after excluding overlapped articles. In the results of the text mining analysis in this study, first, the top 10 keywords mentioned in the highest frequency in every section(July 1^st 2008-February 28^th 2022) were shown in the order of long-term care institution for the elderly, fraudulent claims, National Health Insurance Service, Long-Term Care Insurance for the Elderly, long-term care benefits(expenses), elderly care facilities, The Ministry of Health & Welfare, the elderly, report, and reward(payment). Second, in the results of the N-gram analysis, they were shown in the order of long-term care benefits(expenses) and fraudulent claims, fraudulent claims and long-care institution for the elderly, falsehood and fraudulent claims, report and reward(payment), and long-term care institution for the elderly and report. Third, the analysis of TF-IDF was similar to the results of the frequency analysis while the rankings of report, reward(payment), and increase moved up. Based on such results of the analysis above, this study presented the future direction for the prevention of fraudulent claims of long-term care institutions for the elderly.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.313-325
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• 2022

The purpose of this study is to examine the changes in the elderly healthcare app market through text mining analysis and to present basic data for activating elderly healthcare apps. Data collection was conducted on Naver, Daum, blog web, and cafe. As for the research method, text mining, TF-IDF(Term frequency-inverse document frequency), emotional analysis, and semantic network analysis were conducted using Textom and Ucinet6, which are big data analysis programs. As a result of this study, a total of six categories were finally derived: resolving the healthcare app information gap, convergence healthcare technology, diffusion media, elderly healthcare app industry, social background, and content. In conclusion, in order for elderly healthcare apps to be accepted and utilized by the elderly, they must have a good diffusion infrastructure, and the effectiveness of healthcare apps must be maximized through the active introduction of convergence technology and content development that can be easily used by the elderly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.47-51
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• 2021

In this study, we try to find out and analyze the results of reduced self-esteem and loss using text mining. Physical health is important, of course, but these days, mental health is considered more important. In order for the mind to be healthy, it is important to have self-esteem and self-confidence first. Self-esteem decreases, and if lost, it directly leads to depression. If depression is severe, the worst will lead to self-harm and suicide. However, more and more people are committing suicide these days because both ordinary people and entertainers cannot overcome depression. For this reason, the seriousness of depression and loss of self-esteem are also considered important and become an issue. Therefore, we want to collect data for a certain period of time through Naver, Instagram, and Twitter searches and extract the words of the data to anticipate and analyze the cause of loss of self-esteem, how serious the recent depression is, and what the consequences of loss of self-esteem are.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.841-849
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• 2023

Construction projects have risks due to various factors such as construction delays and construction accidents. Based on these construction risks, the method of calculating the construction period of the construction project is mainly made by subjective judgment that relies on supervisor experience. In addition, unreasonable shortening construction to meet construction project schedules delayed by construction delays and construction disasters causes negative consequences such as poor construction, and economic losses are caused by the absence of infrastructure due to delayed schedules. Data-based scientific approaches and statistical analysis are needed to solve the risks of such construction projects. Data collected in actual construction projects is stored in unstructured text, so to apply data-based risks, data pre-processing involves a lot of manpower and cost, so basic data through a data classification model using text mining is required. Therefore, in this study, a document-based data generation classification model for risk management was developed through a data classification model based on SVM (Support Vector Machine) by collecting construction project documents and utilizing text mining. Through quantitative analysis through future research results, it is expected that risk management will be possible by being used as efficient and objective basic data for construction project process management.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.309-317
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• 2023

The purpose of this paper is to investigate how Japan tried to dominate Chosen through educational policies by analyzing three official English texts published by the Japanese Government-General of Korea: the First Chosen Educational Ordinance declared in 1911, the Manual of Education of Koreans(1913), and the Manual of Education in Chosen 1920(1920). In order to pursue this purpose, the present study carried a corpus-based diachronic analysis, rather then a qualitative analysis. Facilitating text analytics such as Word Cloud and CONCOR, this paper derived the following results: First, the first Chosen Educational Ordinance(1911) includes overall educational regulations, curriculum, and operations of schools. Second, the Manual of Education of Koreans(1913) contains the educational medium and contents on how to educate. Finally, it can be proposed that the Manual of Education in Chosen 1920(1920) contains specific implementation of education and the subject of education.

• Smart Media Journal
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• v.12 no.11
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• pp.185-197
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• 2023

This study was conducted using text mining and network theory to extract useful information for application for occupancy and performance of permit tasks contained in the permit contents from the permit register, which is used only for the simple purpose of recording occupancy permit information. Based on text mining, we analyzed and compared the frequency of vocabulary occurrence and topic modeling in five regions, including Seoul, Gyeonggi, Gyeongsang, Jeolla, Chungcheong, and Gangwon, as well as normalization processes such as stopword removal and morpheme analysis. By applying four types of centrality algorithms, including stage, proximity, mediation, and eigenvector, which are widely used in network theory, we looked at keywords that are in a central position or act as an intermediary in the network. Through a comprehensive analysis of vocabulary appearance frequency, topic modeling, and network centrality, it was found that the 'installation' keyword was the most influential in all regions. This is believed to be the result of the Ministry of Environment's permit management office issuing many permits for constructing facilities or installing structures. In addition, it was found that keywords related to road facilities, flood control facilities, underground facilities, power/communication facilities, sports/park facilities, etc. were at a central position or played a role as an intermediary in topic modeling and networks. Most of the keywords appeared to have a Zipf's law statistical distribution with low frequency of occurrence and low distribution ratio.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.69-79
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• 2024

Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.

• Journal of Technology Innovation
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• v.25 no.3
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• pp.1-24
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• 2017

With the rise of internet of things (IoT), there have been several studies to analyze the technological trend and technological convergence. However, previous work have been relied on the qualitative work that investigate the IoT trend and implication for future business. In response, this study considers the patent information as the proxy measure of technology, and conducts a quantitative and analytic approach for analyzing technological convergence using patent co-classification analysis and text mining. First, this study investigate the characteristics of IoT business, and characterize IoT business into four dimensions: device, network, platform, and services. After this process, total 923 patent classes are classified into four types of IoT technology group. Since most of patent classes are classified into device technology, we developed a co-classification network for both device technology and all technologies. Patent keywords are also extracted and these keywords are also classified into four types: device, network, platform, and services. As a result, technologies for several IoT devices such as sensors, healthcare, and energy management are derived as a main convergence group for the device network. For the total IoT network, base network technology plays a key role to characterize technological convergence in the IoT network, mediating the technological convergence in each application area such as smart healthcare, smart home, and smart grid. This work is expected to effectively be utilized in the technology planning of IoT businesses.