• 스마트미디어저널
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• 제10권2호
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• pp.38-47
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• 2021

본 연구는 주소를 둘러싼 국내외 환경변화 속에서 관계 법령 개정 및 시범사업 등에 의해 본격적으로 도입이 이루어지고 있는 사물주소에 대한 언론보도를 분석하였다. 네이버 뉴스 플랫폼에서 2018년 4월부터 2020년 9월까지 기간동안 '사물주소'라고 검색하여 수집된 언론보도기사의 제목과 원문을 수집하여 토픽 모델링 및 네트워크 분석을 실시하였다. 분석 결과, 보도주제는 4가지 유형으로 '사물주소체계 추진', '사물주소 부여대상 실증', '도로명주소 사용 개선', '주소 활성화를 위한 교육·홍보'로 나타났으며, 해당 기간동안 '사물주소 부여 실증' 주제가 주요 의제였음을 확인하였다. 분석 결과를 행정안전부의 「제3차 주소정책 기본계획(2018-2022)」과 비교하여 정책적 시사점을 제시하였다.

• 한국프랜차이즈경영연구
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• 제13권4호
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• pp.1-12
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• 2022

Purpose: In 2020, the franchise industry accomplished a significant growth compared to the previous year, as the number of franchise companies increased by 9.0% while the number of franchise brands increased by 12.5%. Despite growth in size, the Korean franchise industry underwent many negative incidents, such as franchise ownership sales to private equity funds, that led to deterioration of businesses. From this point of view, this study aims to make various proposals to help policy makers develop franchise industry policies by analyzing trends of the current and previous presidential administrations' franchise policies and regulations using newspaper articles. Research design, data and methodology: A total of 7,439 articles registered in Naver API from February 25, 2013 to November 29, 2021 were extracted. Among them, 34 unrelated video articles were deleted, and a total of 7,405 articles from both administrations were used for analysis. The R package was used for word frequency analysis, word clouding, word correlation analysis, and LDA (Latent Dirichlet Allocation) topic modeling. Results: The keyword frequency analysis shows that the most frequently mentioned keywords during the previous administration include 'no-brand', 'major company', 'bill', 'business field', and 'SMEs', and those mentioned during the current administration include 'industry' and 'policy'. As a result of LDA topic modeling, 9 topics such as 'global startups' and 'job creation' from the previous administration, and 10 topics such as 'franchise business' and 'distribution industry' from the current administration were derived. The results of LDAvis showed that the previous administration operated a policy based on mutual growth of large and small businesses rather than hostile regulations in the franchise business, whereas the current administration extended the regulation related to franchise business to the employment sector. Conclusions: The analysis of past two administrations' franchise policy, it can be suggested that franchisors and franchisees may complement each other in developing the Fair Transactions in Franchise Business Act and achieving balanced growth. Moreover, political support is needed for sound development of franchisors. Limitations and future research suggestions are presented at the end of this study.

• 정보과학회 논문지
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• 제44권11호
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• pp.1138-1148
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• 2017

토픽 추출은 문서 집합으로부터 그 문서 집합을 대표하는 토픽을 자동 추출하는 기술이며 자연어 처리의 중요한 연구 분야이다. 대표적인 토픽 추출 방법으로는 잠재 디리클레 할당과 단어 군집화 기반 토픽 추출방법이 있다. 그러나 이러한 방법의 문제점으로는 토픽 중복 문제와 토픽 혼재 문제가 있다. 토픽 중복 문제는 특정 토픽이 여러 개의 토픽으로 추출되는 문제이며, 토픽 혼재 문제는 추출된 하나의 토픽 내에 여러 토픽이 혼재되어 있는 문제이다. 이러한 문제를 해결하기 위하여 본 연구에서는 토픽 중복 문제에 대해 강건한 잠재 디리클레 할당으로 토픽을 추출하고 단어 간 유사도를 이용하여 토픽 분리 및 토픽 병합의 단계를 거쳐 최종적으로 토픽을 보정하는 방법을 제안한다. 실험 결과 제안 방법이 잠재 디리클레 할당 방법에 비해 좋은 성능을 보였다.

• 한국전자거래학회지
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• 제23권4호
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• pp.19-39
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• 2018

최근 가짜 뉴스가 분야를 막론하고 전 세계에서 주목을 받고 있으며, 현대경제연구원에서는 이러한 가짜 뉴스로 인한 피해 규모가 연간 약 30조 900억원에 달하는 것으로 추산하였다. 정부에서는 "가짜 뉴스 찾기"를 주제로 "인공지능 R&D 챌린지" 대회를 개최하여 가짜 뉴스를 가려낼 인공지능 원천기술 개발에 대한 첫 걸음을 내딛고 있으며, 민간 차원에서도 다양한 분야에서 팩트 체크 서비스가 제공되고 있다. 학계에서도 가짜 뉴스를 탐지하기 위한 시도가 전문가 기반, 집단지성 기반, 인공지능 기반, 시맨틱 기반 등으로 활발하게 이루어지고 있다. 하지만 이러한 시도는 조작의 정밀도가 높을수록 뉴스 자체에 대한 분석만으로 진위 여부를 식별하기가 더욱 어렵다는 한계를 경험하고 있으며, 가짜 뉴스 탐지 모델의 정확도가 과평가된 경향을 보이고 있다. 따라서 본 연구에서는 가짜 뉴스 탐지 모델 정확도의 공정성을 확보하고, 뉴스의 내용뿐만 아니라 해당 뉴스에 대한 반응으로 자연적으로 발생한 광범위한 소셜 데이터를 활용하여 뉴스의 진위 여부를 판정하는 방안을 제안하고자 한다.

• Journal of Information Technology Applications and Management
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• 제25권1호
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• pp.19-32
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• 2018

Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection method using Artificial Intelligence techniques over the past years. But, unfortunately, there have been no prior studies proposed an automated fake news detection method for Korean news. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (Topic Modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as multiple discriminant analysis, case based reasoning, artificial neural networks, and support vector machine can be applied. To validate the effectiveness of the proposed method, we collected 200 Korean news from Seoul National University's FactCheck (http://factcheck.snu.ac.kr). which provides with detailed analysis reports from about 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

• 한국멀티미디어학회논문지
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• 제22권2호
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• pp.250-259
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• 2019

Big data processing technology and artificial intelligence (AI) are increasingly attracting attention. Natural language processing is an important research area of artificial intelligence. In this paper, we use Korean news articles to extract topic distributions in documents and word distribution vectors in topics through LDA-based Topic Modeling. Then, we use Word2vec to vector words, and generate a weight matrix to derive the relevance SCORE considering the semantic relationship between the words. We propose a way to recommend documents in order of high score.

• International journal of advanced smart convergence
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• 제10권3호
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• pp.72-80
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• 2021

Metaverse is an immersive 3D virtual environment, a true virtual artificial community in which avatars act as the user's alter ego and interact with each other. If we do not manage the hype for the metaverse, which has recently been receiving a surge in interest, the metaverse will fail to cross the chasm. In this study, to provide stakeholders with insights for the successful introduction and growth of the 3D immersive next-generation virtual world, metaverse, we analyzed user-side interest, media-side interest, and research-side interest. For this purpose, in this study, search traffic, news frequency and topic, and research article frequency and topic were analyzed. The methodology and results of this study are expected to provide insight for the stable success of metaverse transformation and the coexistence of the real world and the virtual world through hyper-connection and hyper-convergence.

• 대한간호학회지
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• 제53권1호
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• pp.55-68
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• 2023

Purpose: The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. Methods: After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' Conclusion: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

• 한국농촌간호학회지
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• 제18권2호
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• pp.113-124
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• 2023

Purpose: The number of households vulnerable to isolation increases rapidly as social ties decrease, raising concerns about the associated increase in lonely deaths. This study aimed to identify issues related to lonely deaths by analyzing South Korean news articles; and to provide evidence for their use in preventing and managing lonely deaths via community nursing. Methods: This exploratory study analyzed the structure and trends of meaning of lonely deaths by identifying the association between keywords in news articles and lonely deaths. In this study, we searched for all news articles on lonely deaths, covering the period from January 1, 2010, to May 31, 2023. Data preprocessing and purification were conducted, followed by top-keyword extraction, keyword network analysis and topic modeling. The retrieved articles were analyzed using R and Python software. Results: Four main topics were identified: "discovering and responding to lonely death cases", "lonely deaths ending in lonely funerals", "supportive policies to prevent lonely deaths among of older adults", and "local government activities to prevent lonely deaths and support vulnerable populations." Conclusion: Based on these findings, it can be concluded that lonely death is a complex social phenomenon that can be prevented if society shows concern and care. Education related to lonely deaths should be included in nursing curricula for concrete action plans and professional development.

• 한국콘텐츠학회논문지
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• 제21권3호
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• pp.10-22
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• 2021

모바일 뉴스 소비가 증가하고 있지만 지상파 방송사 뉴스 앱의 이용률은 낮다. 이 연구는 지상파 방송사 뉴스 앱의 개선에 필요한 요소를 찾기 위해 20대 이용자를 대상으로 사용성 평가를 수행했다. 모바일 뉴스 앱에서 제공되는 모바일 뉴스 콘텐츠 형식(기존 뉴스 형식, 재가공 형식, 뉴미디어 전용 형식)별로 효율성, 유효성, 만족도를 통해 문제점을 평가했고, 뉴스 주제(경성 뉴스, 연성 뉴스)와 방송사(MBC, SBS)의 차이를 포함해 분석했다. 실험 결과 모바일 뉴스 콘텐츠 형식에 따른 사용성에서는 유사한 문제점이 공통적으로 발견되었고 모바일 뉴스 콘텐츠 형식의 차이는 차별화된 사용성을 제공하지 않는 것으로 나타났다. 그러나 뉴스 주제 차원에서는 경성 뉴스와 연성 뉴스 여부에 따라 각 항목별로 다른 사용성이 드러났다. 이에 따라 이 연구는 뉴스 제작자들이 모바일 뉴스 콘텐츠를 제작할 때 뉴스 콘텐츠 형식보다는 내용 차원의 개선을 할 필요가 있다는 실무적인 함의를 제공한다.