• Journal of the Korean Society of Physical Medicine
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• v.16 no.2
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• pp.91-100
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• 2021

PURPOSE: This study analyzed the trends and characteristics of shoulder rehabilitation research through keyword analysis, and their relationships were modeled using text mining techniques. METHODS: Abstract data of 10,121 articles in which abstracts were registered on the MEDLINE of PubMed with 'shoulder' and 'rehabilitation' as keywords were collected using python. By analyzing the frequency of words, 10 keywords were selected in the order of the highest frequency. Word-embedding was performed using the word2vec technique to analyze the similarity of words. In addition, the groups were classified and analyzed based on the distance (cosine similarity) through the t-SNE technique. RESULTS: The number of studies related to shoulder rehabilitation is increasing year after year, keywords most frequently used in relation to shoulder rehabilitation studies are 'patient', 'pain', and 'treatment'. The word2vec results showed that the words were highly correlated with 12 keywords from studies related to shoulder rehabilitation. Furthermore, through t-SNE, the keywords of the studies were divided into 5 groups. CONCLUSION: This study was the first study to model the keywords and their relationships that make up the abstracts of research in the MEDLINE of Pub Med related to 'shoulder' and 'rehabilitation' using text-mining techniques. The results of this study will help increase the diversifying research topics of shoulder rehabilitation studies to be conducted in the future.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.115-121
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• 2022

In this paper, we analyze trends in the use of mobile forensic technology, focusing on cases where mobile forensics are used, and we predict the development of future mobile forensics technology using linear regression used in future prediction models. For the current status and outlook analysis, we extracted a total of 8 variables by analyzing 1,397 domestic and foreign mobile forensics-related cases and newspaper articles. We analyzed the prospects for each variable using the year of occurrence as an independent variable, seven variables such as text (text message usage information), communication information (cell phone communication information), Internet usage information, messenger usage information, stored files, GPS, and others as dependent variables. As a result of the analysis, among various aspects of the use of mobile devices, the use of Internet usage information, messenger usage information, and data stored in mobile devices is expected to increase. Therefore, it is expected that continuous research on technologies that can effectively extract and analyze characteristic information of mobile devices such as file systems, the Internet, and messengers will be needed As mobile devices increase performance and utilization in the future and security technology.

• Korean Medical Education Review
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• v.24 no.2
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• pp.93-102
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• 2022

We use the concept of the "doctor's role" as a guideline for developing medical education programs for medical students, residents, and doctors. Therefore, we should regularly reflect on the times and social needs to develop a clear sense of that role. The objective of the present study was to understand the knowledge structure related to doctor's job competencies in Korea. We analyzed research trends related to doctor's job competencies in Korea Citation Index journals using text network analysis through an integrative approach focusing on identifying social issues. We finally selected 1,354 research papers related to doctor's job competencies from 2011 to 2020, and we analyzed 2,627 words through data pre-processing with the NetMiner ver. 4.2 program (Cyram Inc., Seongnam, Korea). We conducted keyword centrality analysis, topic modeling, frequency analysis, and linear regression analysis using NetMiner ver. 4.2 (Cyram Inc.) and IBM SPSS ver. 23.0 (IBM Corp., Armonk, NY, USA). As a result of the study, words such as "family," "revision," and "rejection" appeared frequently. In topic modeling, we extracted five potential topics: "topic 1: Life and death in medical situations," "topic 2: Medical practice under the Medical Act," "topic 3: Medical malpractice and litigation," "topic 4: Medical professionalism," and "topic 5: Competency development education for medical students." Although there were no statistically significant changes in the research trends for each topic over time, it is nonetheless known that social changes could affect the demand for doctor's job competencies.

• Journal of Information Technology Services
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• v.22 no.3
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• pp.141-165
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• 2023

With the advent of the fourth industrial revolution, technologies such as the Internet of Things, artificial intelligence and cloud computing are developing rapidly, and smart homes enabled by these technologies are rapidly gaining popularity. To gain a competitive advantage in the global market, companies must understand the differences in consumer needs in different countries and cultures and develop corresponding business strategies. Therefore, this study conducts a comparative analysis of consumer reviews of smart homes in South Korea and China. This study collected online reviews of SmartThings, ThinQ, Msmarthom, and MiHome, the four most commonly used smart home apps in Korea and China. The collected review data is divided into satisfied reviews and dissatisfied reviews according to the ratings, and topics are extracted for each review dataset using LDA topic modeling. Next, the extracted topics are classified according to five evaluation factors of Perceived Usefulness, Reachability, Interoperability,Trustness, and Product Brand proposed by previous studies. Then, by comparing the importance of each evaluation factor in the two datasets of satisfaction and dissatisfaction, we find out the factors that affect consumer satisfaction and dissatisfaction, and compare the differences between users in Korea and China. We found Trustness and Reachability are very important factors. Finally, through language network analysis, the relationship between dissatisfied factors is analyzed from a more microscopic level, and improvement plans are proposed to the companies according to the analysis results.

• Smart Media Journal
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• v.11 no.11
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• pp.75-83
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• 2022

NIST in the United States has developed SCAP, a protocol that enables automated inspection and management of security vulnerability using existing standards such as CVE and CPE. SCAP operates by creating a checklist using the XCCDF and OVAL languages and running the prepared checklist with the SCAP tool such as the SCAP Workbench made by OpenSCAP to return the check result. SCAP checklist files for various operating systems are shared through the NCP community, and the checklist files include ID, title, description, and inspection method for each item. However, since the inspection items are simply listed in the order in which they are written, so it is necessary to classify and manage the items by type so that the security manager can systematically manage them using the SCAP checklist file. In this study, we propose a method of extracting the description of each inspection item from the SCAP checklist file written in OVAL language, classifying the categories through a machine learning model, and outputting the SCAP check results for each classified item.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.8
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• pp.399-408
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• 2016

As entering the Big Data age, utilization of Big Data is also increasing in the intellectual property sector. Meanwhile, the purpose of a trademark which distinguishes the source of the goods essentially is to enable the public to recognize the goods. Big Data technologies which is recently becoming a issue can be used as a tool to judge consumer's awareness of the trademark. It was difficult for judgment of trademark awareness through traditional ways. As a new way, survey methodology has bee received attention, and it was applied to the field of trademark law. However, various problems such as cost, time, objectivity, and fairness were observed. In order to overcome theses limitations, this study proposes new way utilizing big data analytics for judgment on consumer's awareness of the trademark. This new way will not only contribute to enhancing the objectivity of judging trademark awareness but also utilized to support for related legal judgments.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.12-17
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• 2019

FAQ(Frequently Asked Questions) 질의 응답 시스템은 자주 묻는 질문과 답변을 정의하고, 사용자 질의에 대해 정의된 답변 중 가장 알맞는 답변을 추론하여 제공하는 시스템이다. 정의된 대표 질문 및 대응하는 답변을 클래스(Class)라고 했을 때, FAQ 질의 응답 시스템은 분류(Classification) 문제라고 할 수 있다. 종래의 FAQ 분류는 동일 클래스 내 동의 문장(Paraphrase)에서 나타나는 공통적인 특징을 통해 분류 문제를 학습하였으나, 이는 비슷한 단어 구성을 가지면서 한 두 개의 단어에 의해 의미가 다른 문장의 차이를 구분하지 못하며, 특히 서로 다른 클래스에 속한 학습 데이터 간에 비슷한 의미를 가지는 문장이 존재할 때 클래스 분류에 오류가 발생하기 쉬운 문제점을 가지고 있다. 본 논문에서는 이 문제점을 해결하고자 서로 다른 클래스 내의 학습 데이터 문장들이 상이한 클래스임을 구분할 수 있도록 클래스 일치 여부(Class coincidence classification) 문제를 결합 학습(Jointly learning)하는 기법을 제안한다. 동일 클래스 내 학습 문장의 무작위 쌍(Pair)을 생성 및 학습하여 해당 쌍이 같은 클래스에 속한다는 것을 학습하게 하면서, 동시에 서로 다른 클래스 간 학습 문장의 무작위 쌍을 생성 및 학습하여 해당 쌍은 상이한 클래스임을 구분해 내는 능력을 함께 학습하도록 유도하였다. 실험을 위해서는 최근 발표되어 자연어 처리 분야에서 가장 좋은 성능을 보이고 있는 BERT 의 텍스트 분류 모델을 이용했으며, 제안한 기법을 적용한 모델과의 성능 비교를 위해 한국어 FAQ 데이터를 기반으로 실험을 진행했다. 실험 결과, 분류 문제만 단독으로 학습한 BERT 기본 모델보다 본 연구에서 제안한 클래스 일치 여부 결합 학습 모델이 유사한 문장들 간의 차이를 구분하며 유의미한 성능 향상을 보인다는 것을 확인할 수 있었다.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.510-513
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• 2019

감정분석은 텍스트에서 나타난 저자 혹은 발화자의 태도, 의견 등과 같은 주관적인 정보를 추출하는 기술이며, 여론 분석, 시장 동향 분석 등 다양한 분야에 두루 사용된다. 감정분석 방법은 사전 기반 방법, 기계학습 기반 방법 등이 있다. 본 논문은 사전 기반 감정분석에 필요한 한국어 감정사전 자동 구축 방법을 제안한다. 본 논문은 영어 감정사전으로부터 한국어 감정사전을 자동으로 구축하는 방법이며, 크게 세 단계로 구성된다. 첫 번째는 한영 병렬 말뭉치를 이용한 한영 이중언어 사전을 구축하는 단계이고, 두 번째는 한영 이중언어 사전을 통한 한영 이중언어 그래프를 생성하는 단계이며, 세 번째는 영어 단어의 감정값을 한국어 BPE의 감정값으로 전파하는 단계이다. 본 논문에서는 제안된 방법의 유효성을 보이기 위해 사전 기반 한국어 감정분석 시스템을 구축하여 평가하였으며, 그 결과 제안된 방법이 합리적인 방법임을 확인할 수 있었으며 향후 연구를 통해 개선한다면 질 좋은 한국어 감정사전을 효과적인 방법으로 구축할 수 있을 것이다.

• Korea Journal of Hospital Management
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• v.28 no.2
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• pp.32-43
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• 2023

Purposes: This research is conducted to identify main features and trends of mission, vision and core values in Korean tertiary general hospitals by using text-mining. Methodology: For the study, 45 mission, 112 vision and 190 core values are collected from 45 tertiary general hospitals' homepages in 2022 and use word frequency analysis and Leyword co-occurrence analysis. Findings: In the tertiary general hospitals' mission, there are high frequency words such as 'health', 'humanity', 'medical treatment', 'education', 'research', 'happiness', 'love', 'best', 'spirit', and mission mainly includes the content of contributing humanity's health and happiness with these words. In case of vision, high frequency words are 'hospital', 'medical treatment', 'research', 'lead', 'trust', 'centered', 'patient', 'best', 'future'. By using these words in vision, it represents the definition and characteristics of vision such as ideal organizations in the future, goals and targets. As a result of the Leyword co-occurrence analysis, vision includes the content of 'high-tech medical treatment', 'special care for patients', 'leading education and research', 'the highest trust with customer', 'creative talents training'. -astly, the high frequency word-pairs in core values are 'social distribution', 'innovation pursuit', 'cooperation and harmony', and it defines standards of behavior for organizations. Practical Implication: To correct the problems of vision, mission and core values from findings, firstly, it needs for Korean tertiary general hospitals to use the words that can explain organization's identity and differentiate others in their mission. Secondly, considering strengthening the role of hospitals in their community and the importance of members in organizations, it is necessary to establish vision with considering community and members to activate vision effectively. Thirdly, because there are no specific guidelines of establishing mission, vision and core values for healthcare organizations, this research concepts and results could be utilized when other organizations establish mission, vision and core values.

• The Journal of Bigdata
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• v.7 no.2
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• pp.103-111
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• 2022

There are many institutions, organizations, and sites related to responding to infectious diseases, but as the pandemic situation such as COVID-19 continues for years, there are many changes in the initial and current aspects, and accordingly, policies and response systems are evolving. As a result, regional gaps arise, and various problems are scattered due to trust, distrust, and implementation of policies. Therefore, in the process of analyzing social data including information transmission, Twitter data, one of the major social media platforms containing inaccurate information from unknown sources, was developed to prevent facts in advance. Based on social data, which is unstructured data, an algorithm that can automatically detect infectious disease threats is developed to create an objective basis for responding to the infectious disease crisis to solidify international competitiveness in related fields.