• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.409-418
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• 2015

With the development of mobile devices and Internet, information exchange has actively been made through SNS and Blogs. Blogs are widely used as a space where people share their experience after their visit to tourist attractions. We propose a method of recommending associated tourist attractions based on tourists' opinions using issue analysis, association analysis, and sentimental analysis for various online reviews including news in order to help to develop tour products and policies. The result shows that north area of Chungbuk province has been selected as issue attractions, and associated attractions/keywards have been identified for given well-known attraction. Positive/negative opinion for review texts has been analyzed and user can grasp the reason for the sentiments. Multidimensional analysis technique has been integrated to derive additional sophisticated insights and various policy proposal for smart tourism.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.487-492
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• 2018

4차산업 혁명의 여파로 국내에서는 다양한 분야에 인공지능과 빅데이터 기술을 활용하여 이전에 시행 중인 다양한 서비스 분야에 기술적 접목과 보완을 시도하고 있다. 특히 금융권에서 자금을 빌린 기업들을 대상으로 여신 안정성을 확보하고 선제적인 대응을 위해 온라인 뉴스기사들과 SNS 데이터 등을 이용하여 부실가능성을 예측하고 실제 업무에 도입하려는 시도들이 국내 주요 은행들을 중심으로 활발히 진행 중이다. 우리는 국내의 국책은행에서 수행한 비정형 데이터 기반의 기업의 부실징후 예측 시스템 개발 과정에서 시도된 다양한 분석 방법과 결과 그리고 과정 중에 발생한 문제점들에 관해 기술하고 관련 이슈들에 관하여 다룬다. 결과적으로 본 논문은 레이블이 없는 대량의 기사들에 레이블을 달기 위한 자동 태거(tagger) 개발과 뉴스 기사 예측 결과로부터 부실 가능성을 예측하기 위한 모델 및 성능 면에서 기사 예측 정확도 92%(AUC 0.96) 및 부실 가능성 기업 예측에서도 정형 데이터 분석결과에 견줄만한 성과를 이루었고 이에 관해 보고한다.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.11-21
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• 2021

The purpose of this study is to explore and analyze how the Covid-19 pandemic has affected the reporting practices and news production of the reporters working with Korean media over the past one year. To this end, this study has conducted in-depth interviews with reporters working with daily newspapers, news agencies, and broadcasting companies. The analysis of the interviews resulted in the following generalizations. The reporters are working at home, which was never experienced before the Covid-19 pandemic, and they have difficulties in covering their reporting beats because they have little or no access to contact points. The reporters rely heavily on indirect coverage and online briefings via phones or SNS because they have difficulties in meeting news sources in person. As a result, the diversity of news contents and the media's on-site monitoring functions has been severely weakened. In addition, the reporters have no chances to both exchange ideas with fellow reporters and to transfer the know-how of collecting news items to their juniors. This paper has also discussed the disruption of practices that the ongoing Covid-19 has brought to the media ecosystem from a variety of perspectives.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.138-147
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• 2018

The network analysis is done to explore what kind if issues are diffused about earthquake and the role of social media. The types of disaster information is classified into formal and informal. The role of actor is classified based on the concentrativeness and the diffusion of issue. Youttube is functioned as a formal channel and an informal channel when disaster happened. In case of government's video, issue contractiveness is high but the diffusion is low. In case of media's video, issue contractiveness and the diffusion are all high. In case of individual channel, issue contractiveness is low, but diffusion is high. In disaster, youtube is a tool to respread the disaster issue. Government needs to try diffusion of government's news actively in disaster.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.209-216
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• 2024

Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a variety of unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transaction prices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a News Sentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index, the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutral sentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used for real estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM prediction model, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117 for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724, and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254, and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413, and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.4
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• pp.169-176
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• 2015

Recently smartphone market is rapidly growing and application market has also grown significantly. Mobile applications have been provided in various forms, such as education, game, SNS, weather and news. And It is distributed through a variety of distribution channels. Malicious applications deployed with malicious objectives are growing as well as applications that can be useful in everyday life well. In this study, Events from a malicious application that is provided by the normal application deployment and Android MalGenome Project through the open market were extracted and analyzed. And using the results, We create a model to determine whether the application is malicious. Finally, model was evaluated using a variety of statistical method.

• Korean journal of communication and information
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• v.81
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• pp.299-328
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• 2017

This study investigated news comprehension via the social media by comparing the reading of a news story on the news paper. A news story on the social media was suggested to present information in a conversational form, which differs from a traditional reporting style. To compare the different forms of news information presentation, two conditions were created: in a control condition, a news story was written in a traditional reporting form. In the experimental condition, the same news story was constructed in a conversational form. Participants were assigned randomly in one of two conditions. They read the news story and afterwards, they were asked to recall firstly, the core idea of the news story, secondly the whole news story, and finally to answer to the 10 questions that assessed how well they learned from the news story. Participants' responses were content-analyzed and produced six variables, the extent to recall the core idea, the extent to recall the whole story, the extent to recall wrong information, the extent to recall additional information, the extent to recall causally related contents in general, and finally the extent to recall causally related contents in story-specific. Analyses on the six variables revealed that the group in the news paper condition recalled more core idea, the whole story, and additional information than the group in the social media. But the news paper condition recalled less of wrong information than the group in the social media condition. Additionally, the news paper condition learned more than the group in the social media. Regarding the recall of causally related contents, the general causal relationships were recalled more in the group in the social media condition but the story specific causal relationships were recalled more in the group in the news paper condition. The findings seemingly indicated that a traditional news reporting contributes to news story comprehension more than the conversational form. Authors however added discussions and advised that the findings needed to be read under caution.

• Journal of Korean Society of Archives and Records Management
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• v.19 no.1
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• pp.167-194
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• 2019

This study aims to examine the change of awareness on presidential archives and records before and after impeachment by analyzing user frames. To achieve the goal of this study, prior studies of frame analysis were reviewed, and tweets of presidential archives and records before and after the impeachment were collected. This study conducted an analysis of Twitter and news extracted from Twitter using user frames and determined the differences between each frame over time. Afterward, five frames were set up to be used for the research through prior research and Twitter network analysis; changes in frames over time were examined by analyzing Twitter and news extracted from Twitter. Through such frame analysis, changes in the frame of presidential archives and records before and after the impeachment were examined, changes in public perception of presidential archives and records were identified, and areas of interest were determined. This study is significant as it identified changes in the public perception of presidential archives and records as well as in the areas of interest for the general public.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.363-384
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• 2024

This study was conducted to examine the user's experiences with the public library in Daejeon using big data analysis, focusing on the text mining technique. To know this, first, the overall evaluation and perception of users about the public library in Daejeon were explored by collecting data on social media. Second, through analysis using online news articles, the pending issues that are being discussed socially were identified. As a result of the analysis, the proportion of users with children was first high. Next, it was found that topics through LDA analysis appeared in four categories: 'cultural event/program', 'data use', 'physical environment and facilities', and 'library service'. Finally, it was confirmed that keywords for the additional construction of libraries and complex cultural spaces and the establishment of a library cooperation system appeared at the core in the news article data. Based on this, it was proposed to build a library in consideration of regional balance and to create a social parenting community network through business agreements with childcare and childcare institutions. This will contribute to identifying the policy and social trends of public libraries in Daejeon and implementing data-based public library operations that reflect local community demands.