• 한국산학기술학회논문지
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• 제19권10호
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• pp.176-182
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• 2018

본 연구는 건설 안전사고에 대한 트랜드 분석을 위해 LDA(Latent Dirichlet Allocation) 기반의 토픽모델링(Topic Modeling)을 제시하여 분석하고자 한다. 특히, 건설산업의 안전사고를 예방하기 위해 제시되고 있는 기존의 다양한 정형데이터 분석에서 벗어난 비정형 데이터 분석 기반의 토픽 모델링을 통해 건설 안전사고 주요 핵심 키워드의 흐름에 대해 파악이 가능하다. 본 방법론을 적용하기 위해 540개의 건설 안전사고 관련 뉴스데이터를 수집하였다. 이를 기반으로, 10가지 토픽과 각 토픽 내의 10가지 키워드를 통해 주요 이슈를 도출하였고 각 토픽에 대한 2017년 1월부터 2018년 2월까지의 뉴스 데이터를 월별 시계열 분석을 통해 향후 토픽에 관한 이슈를 예측한다. 본 연구를 바탕으로 향후 건설 안전사고의 다양한 이슈를 선제적으로 예측하고 이를 기반으로 건설 안전사고 정책과 연구에 좋은 방향을 제시할 것으로 판단한다.

• 한국주거학회논문집
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• 제2권2호
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• pp.41-53
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• 1991

This study discussed the change of housing environmental problems from the early 1900s to the present.The reason is to find the solution of serious housing environment problems. The documentary research method was used for this study.Articles of content analysis(N= 1129)were published in 1920(the first edition)to December. 31, 1990 which were The Dong - A daily news article about housing environment. The main content of this study was examined the change, such as the number of whole article by time series and importance of article(column number of article), classification of article subject, and the number of article by subject. On the basis of this data, was made by chronological classification of the change of housing environment problems for 70 years. Since overall results will become supply of right information about housing environment to fur peoples, will provide the oppronment that oneself ran participate the protection of housing environment, and further will take a part solution of housing environment problems.At the future, I am going to design deep analysis of article content by subject.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.627-630
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• 2022

Over the past few years, fake news has become one of the most significant problems. Since it is impossible to prevent people from spreading misinformation, people should analyze the news themselves. However, this process takes some time and effort, so the routine part of this analysis should be automated. There are many different approaches to this problem, but they only analyze the text and messages, ignoring the images. The fake news problem should be solved using a complex analysis tool to reach better performance. In this paper, we propose the approach of training an Artificial Intelligence using an unsupervised learning algorithm, combined with online data parsing tools, providing independence from subjective data set. Therefore it will be more difficult to spread fake news since people could quickly check if the news or article is trustworthy.

• Journal of Platform Technology
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• 제11권3호
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• pp.83-97
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• 2023

본 연구는 시멘틱 네트워크 분석을 통해 방대한 뉴스 기사를 분석하여 창업 트렌드를 파악하고자 하였다. 한국언론진흥재단에서 제공하는 빅카인즈 기사 분석 서비스를 이용해 2013년 1월부터 2022년 12월까지 19개 신문사의 뉴스 기사 330,628건을 종합적으로 분석하였다. 이 연구는 사회적 환경과 글로벌 경제 트렌드가 창업에 미치는 영향을 고려하여 최근 10년 동안 주요 이슈의 변화를 탐구하는 데 중점을 두었다. 또한 코로나-19 팬데믹 전후의 뉴스 기사 수와 이슈 변화를 비교하여 빈도 분석, 관계 분석, 연관어 분석을 통해 창업 트렌드를 시각화 하여 제시하였다. 연구 결과, 창업 연관어의 상위 키워드는 창업의 활성화, 사업화 등이고, 코로나-19와 창업 키워드 간의 상관관계는 선형적인 의미에서 거의 무시할 수 있는 수준이었으나, 팬데믹 기간동안 뉴스 기사 수는 감소하여 영향을 미치는 것으로 나타났다. 특히 가장 많이 언급된 키워드는 중소벤처기업부, 장소는 미국, 인물은 한정화. 기관은 중소벤처기업부로 나타났으며 창업분야는 어떤 분야보다 사회적 이슈에 다각적인 영향을 받고,시기적 접근 빈도가 증가하는 중요한 특징이 나타났다. 본 연구결과는 창업 관련 이슈 및 사건에 대한 이해와 탐구에 필수적인 기초자료를 제공하여 향후 해당 분야 연구주제를 제안할 연구로서의 의미가 있다.

• International Journal of Advanced Culture Technology
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• 제11권2호
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• pp.36-43
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• 2023

This study aims to derive discourse's linguistic meaning, production method, and social practice implications by analyzing news reports on de-facility for people with disabilities. To this end, the discourse was analyzed by applying Fairclough's framework of critical discourse analysis. The subject of analysis is a news article on the de-facility of the disabled on the N portal site, and the analysis period is one year, from January 1 to December 31, 2022. First, as a result of the study, the surface meaning of the news discourse on the de-facility for disabled people was ideological through the seriousness of the problem for disabled people, the poor environment, and the policy of de-facility for disabled people separated from reality. Second, the social meaning of the de-facility news discourse for disabled people appeared from a realistic perspective, such as the structural cause of the problem for disabled people and the need for sensible government policies and measures to practice de-facility for disabled people. Finally, the socio-cultural practical implications of the de-facility news discourse for people with disabilities proposed the development of a systematic and realistic de-facility management manual for the disabled, practical government policy support, and changes in self-support perception for disabled people. The results of this study are expected to help find an alternative direction to reduce the gap between actual policies for de-facility for disabled people and practice in the field in the future.

• 한국정보처리학회논문지
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• 제6권5호
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• pp.1189-1202
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• 1999

As compared with VOD data, NOD article data has the following characteristics: it is created at any time, has a short life cycle, is selected as not one article but several articles by a user, and has high access locality in time. Because of these intrinsic features, user access patterns of NOD article data are different from those of VOD. Thus, building NOD system using the existing techniques of VOD system leads to poor performance. In this paper, we analysis the log file of a currently running electronic newspaper, show that the popularity distribution of NOD articles is different from Zipf distribution of VOD data, and suggest a new popularity model of NOD article data MS-Zipf(Multi-Selection Zipf) distribution and its approximate solution. Also we present a life cycle model of NOD article data, which shows changes of popularity over time. Using this life cycle model, we develop LLBF (Largest Life-cycle Based Frequency) prefetching algorithm and analysis he performance by simulation. The developed LLBF algorithm supports the similar level in hit-ratio to the other prefetching algorithms such as LRU(Least Recently Used) etc, while decreasing the number of data replacement in article prefetching and reducing the overhead of the prefetching in system performance. Using the accurate user access patterns of NOD article data, we could analysis correctly the performance of NOD server system and develop the efficient policies in the implementation of NOD server system.

• 한국컴퓨터정보학회논문지
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• 제29권2호
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• pp.51-59
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• 2024

투자자는 수익의 극대화를 위해 언론사의 기사를 포함한 다양한 정보를 활용하여 투자 전략을 수립한다. 이에 국내 언론사에서도 신뢰도 있는 투자정보를 제공하기 위해, 애널리스트의 종목분석 보고서에 기초한 종목 추천기사를 게재하고 있다. 본 연구에서는 종목 추천기사 게재를 하나의 사건(event)으로 간주하고, XGBoost와 LightGBM 모델을 활용하여 기사 게재 10일 이후 가격의 상승 또는 하락을 예측하는 분류 모델을 제시한다. 또한, 전체 추천종목을 유가증권시장과 코스닥 시장 및 기업규모(대형/소형)에 따라 4가지로 분류하고, 하위 그룹에 따라 모델의 예측 정확도에 차이가 있는지 파악하고자 한다. 학습 결과 전체 모델의 분류 정확도는 XGBoost 75%, LightGBM 71%로 나타났고, 예측 정확도는 유가증권 시장 예측력이 코스닥시장 주식 대비 높게 나타났으며, 대형주의 예측력이 소형주 보다 높게 나타났다. 마지막으로, SHAP(Shapley Additive exPlanations) 분석을 통해 개별 모델의 예측에 중요한 변수를 살펴보고 모델의 해석력을 제고하였다.

• 대한조현병학회지
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• 제23권2호
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• pp.58-64
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• 2020

Objectives: In this study, we conducted an exploratory analysis of the current media trends on schizophrenia using text-mining methods. Methods: First, web-crawling techniques extracted text data from 575 news articles in 10 major newspapers between 2018 and 2019, which were selected by searching "schizophrenia" in the Naver News. We had developed document-term matrix (DTM) and/or term-document matrix (TDM) through pre-processing techniques. Through the use of DTM and TDM, frequency analysis, co-occurrence network analysis, and topic model analysis were conducted. Results: Frequency analysis showed that keywords such as "police," "mental illness," "admission," "patient," "crime," "apartment," "lethal weapon," "treatment," "Jinju," and "residents" were frequently mentioned in news articles on schizophrenia. Within the article text, many of these keywords were highly correlated with the term "schizophrenia" and were also interconnected with each other in the co-occurrence network. The latent Dirichlet allocation model presented 10 topics comprising a combination of keywords: "police-Jinju," "hospital-admission," "research-finding," "care-center," "schizophrenia-symptom," "society-issue," "family-mind," "woman-school," and "disabled-facilities." Conclusion: The results of the present study highlight that in recent years, the media has been reporting violence in patients with schizophrenia, thereby raising an important issue of hospitalization and community management of patients with schizophrenia.

• 지식경영연구
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• 제14권4호
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• pp.1-17
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• 2013

As online communities proliferate, online news sites have received great attention in news media research. Although most of the online news sites provide contents for free, some have adopted the Pay-What-You-Want (PWYW) model by offering a voluntary payment option to the readers. In this study, we investigate the factors which influence subscribers' voluntary payment behavior on an online news site. Drawing upon both the Stimulus-Organism-Response (SOR) framework and the Elaboration Likelihood Model (ELM), we hypothesize that appreciation has a direct effect on the subscribers' voluntary payment behavior, whereas central factors (positive emotional content, cognitive content) and peripheral factors (news sharing, news article length) of the news articles have indirect impacts on voluntary payment behavior through the enhanced appreciation. Based on an empirical analysis of 172 news articles from the Korean online news site that adopted the PWYW pricing model (i.e., Ohmynews.com), we find that appreciation plays a critical role in voluntary payment behavior and that peripheral factors have significant impacts on appreciation. However, the impacts of central factors on appreciation are not found. By identifying influencing factors of subscribers' voluntary payment behavior on online news sites for the first time, this paper suggests a prospective alternative profit model for online news providers faced with fierce competition.

• Asian Journal for Public Opinion Research
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• 제9권1호
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• pp.48-82
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• 2021

This research article attempts to understand the current situation of fake news on social media in India. The study focused on four characteristics of fake news based on four research questions: subject matter, presentation elements of fake news, debunking tool(s) or technique(s) used, and the social media site on which the fake news story was shared. A systematic sampling method was used to select a sample of 90 debunked fake news stories from two Indian fact-checking websites, Alt News and Factly, from December 2019 to February 2020. A content analysis of the four characteristics of fake news stories was carefully analyzed, classified, coded, and presented. The results show that most of the fake news stories were related to politics in India. The majority of the fake news was shared via a video with text in which narrative was changed to mislead users. For the largest number of debunked fake news stories, information from official or primary sources, such as reports, data, statements, announcements, or updates were used to debunk false claims.