• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.341-348
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• 2014

The advance in web accessibility with dissemination of smart phones gives rise to rapid increment of users on social network platforms. Many research projects are in progress to detect events using Twitter because it has a powerful influence on the dissemination of information with its open networks, and it is the representative service which generates more than 500 million Tweets a day in average; however, existing studies to detect events has been used TFIDF algorithm without any consideration of the various conditions of tweets. In addition, some of them detected predefined events. In this paper, we propose the RTFIDF VT algorithm which is a modified algorithm of TFIDF by reflecting features of Twitter. We also verified the optimal section of TF and DF for detecting events through the experiment. Finally, we suggest a system that extracts result-sets of places and related keywords at the given specific time using the RTFIDF VT algorithm and validated section of TF and DF.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.599-606
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• 2017

'Video Commerce' has grown significantly, and is in the era of so-called V-commerce 2.0. Based on this background, this study focused on the link and the possibility of creating synergy between V-commerce 2.0 content and MCN, and examined the linkage strategy considering its characteristics. In conclusion, first, V-Commerce has evolved into the age of 2.0, centered on the characteristics of content that are oriented towards fun and sympathy, beyond the 1.0 era. Second, V-commerce 2.0 content has the characteristic of replacing the sharing and recommendation based on the nature of SNS networks as promotion and purchase enhancement. Therefore, competitiveness as 'content' is relatively important before 'commerce'. Third, V-commerce 2.0 and MCN industry have a strong connection with each other in terms of securing core competitiveness and creating a new profit model. In order to create the synergy between V-Commerce 2.0 and MCN, we proposed the use of big data to reinforce V-Commerce 2.0 customized content competitiveness, building of storytelling marketing and branding, and enhancement of live performance and interactive communication.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.383-390
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• 2014

In recent years, the convergence of IT and IT sightseeing tour has emerged as a fusion of academic disciplines in the future. Convergence study of social data analysis, raising the heat. Social Network Services (SNS) being utilized in many areas of marketing and to apply the case study is also increasing. This study is based u-smart tourist information systems for mobile learning content design. This is the pattern of things in the template library for things to increase the effectiveness of the learning content to mobile learning content to be converted to a. Design of mobile learning content using u-smart things smart phone app (App) and XMI to go through the design process of utilizing the heat. Future through the design process by implementing a mobile learning content to meet information quality tourist information content to create mobile learning content and learning things that can be content to live it up advantage.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.180-185
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• 2015

As the number of document resources is continuously increasing, automatically extracting keyphrases from a document becomes one of the main issues in recent days. However, most previous works have tried to extract keyphrases from words in documents, so they overlooked latent keyphrases which did not appear in documents. Although latent keyphrases do not appear in documents, they can undertake an important role in text summarization and information retrieval because they implicate meaningful concepts or contents of documents. Also, they cover more than one fourth of the entire keyphrases in the real-world datasets and they can be utilized in short articles such as SNS which rarely have explicit keyphrases. In this paper, we propose a new approach that selects candidate keyphrases from the keyphrases of neighbor documents which are similar to the given document and evaluates the importance of the candidates with the individual words in the candidates. Experiment result shows that latent keyphrases can be extracted at a reasonable level.

• Information and Communications Magazine
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• v.30 no.1
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• pp.24-32
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• 2012

우리는 지금 스마트 사회에 살아가고 있다. 언제 어디서든 스마트 디바이스를 통해 기존에 PC에서 하던 작업들을 손쉽게 하고 있다. 한편 스마트폰의 확산으로 이용자 수가 급증하고 있는 소셜네트워크 서비스(SNS)는 이용자들이 자신의 일상적인 이야기를 사이버공간에 게시함으로 인해 개인의 사생활 정보들이 노출되고, 그러한 정보들이 범죄에 악용되는 사례들이 눈에 띄게 증가하고 있다. 또한 SNS를 이용한 악성코드의 유포 및 빠른 전파 등도 새로운 보안위협으로 나타나고 있다. 그 밖에 스마트 기기를 대상으로 한 해킹 및 악성코드 감염 등 위협이 증가하고 있는 형편이다. 본고에서는 스마트 사회의 주요 보안위협을 살펴보고 미국, 유럽, 일본, 호주 등 선진국의 관련 정책 동향과 국내 정책과 실태를 분석하여 새로운 정보보호 정책 수립 방향을 제언하고자 한다. 스마트 사회 위험 요소로 가장 보편적으로 사용되고 있는 스마트폰과 스마트폰을 통해 이용되고 있는 소셜네트워크 서비스, 클라우드 서비스의 보안위협을 제기하고 최근 글로벌 이슈로 떠오르고 있는 빅 데이터 환경의 보안위협을 분석하였다. 스마트 사회의 위협을 대비하고 있는 주요국 정책을 살펴보면, 미국의 경우 사회적 합의를 바탕으로한 감시와 통제를 강화하는 정책을 추진 중에 있으며 유럽의 5개국 EU5(영국, 독일, 프랑스, 스페인, 이탈리아)는 스마트폰 위협을 중심으로 공동 대응 방안을 마련하고 있다. 일본은 스마트 워크중심의 보안대책을 강구하고 있으며 호주는 스마트 사회 보안위협에 대한 국민의 인식제고에 주력하고 있다. 국내의 경우도 스마트 사회의 보안위협에 선제적 대응을 위하여 "스마트 모바일 시큐리티 종합계획"을 수립하여 추진중에 있다. 하지만 보안 실태를 보면 스마트 사회 보안위협에 대한 이용자들의 우려는 높은 반면 기업의 보안 대책 마련에 대한 투자는 여전히 미흡한 상황이다. 향후 우리 사회가 디바이스간 융합을 넘어 모든 사물이 연결되는 초(超)연결(Hyper-Connectivity) 시대로 진화되어 가면 편리성이 증대되는 만큼 더 많은 위협에 우리의 일상이 노출되는 문제가 발생하게 될 것이다. 안전한 미래 사회로 진입하기 위해서는 보다 체계적이고 종합적인 정보보호 정책마련이 필요하다. 본고에서는 이를 위한 정책수립의 방향을 제언했다.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.31-36
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• 2019

A chatbot is an important area of mobile service, which understands informal data of a user as a conversational form and provides a customized service information for user. However, there is still a lack of a service way to fully understand the user's natural language typed query dialogue. Therefore, in this paper, we extract meaningful words, such a region, a food category, and a restaurant name from user's dialogue sentences for recommending a restaurant. and by comparing the extracted words against the contents of the knowledge database that is built from the hashtag for recommending a restaurant in SNS, and provides user target information having statistically much the word-similarity. In order to evaluate the performance of the restaurant recommendation chatbot system implemented in this paper, we measured the accessibility of various user query information by constructing a web-based mobile environment. As a results by comparing a previous similar system, our chabot is reduced by 37.2% and 73.3% with respect to the touch-count and the cutaway-count respectively.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.231-237
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• 2021

Over a decade ago, Krasnova et al. identified the factors that influence Facebook users' self-disclosure. These factors include perceived risks, relationship building, relationship maintenance, self-presentation, and enjoyment. Meanwhile, during the past 10 years, there have been significant changes in terms of function, media, and competition. SNSs have been functionally enhanced, used in mobile environment, and had many competitors. Based on these facts, it is believed that the influence of the factors on self-disclosure is different from those of Krasnova et al. The purpose of this study is to verify through a replication study whether the factors adopted in the study of Krasnova et al. are still important in explaining self-exposure. The study empirically find the result significantly different from those of Krasnova et al. Based on the result, the study provides meaningful implications and suggestions for future research.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.29-41
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• 2021

Recently, there are increasing attempts to utilize deep learning methodology in the fashion industry. Accordingly, research dealing with various fashion-related problems have been proposed, and superior performances have been achieved. However, the studies for fashion style classification have not reflected the characteristics of the fashion style that one outfit can include multiple styles simultaneously. Therefore, we aim to solve the multi-label classification problem by utilizing the dependencies between the styles. A multi-label recognition model based on a graph convolution network is applied to detect and explore fashion styles' dependencies. Furthermore, we accelerate model training and improve the model's performance through transfer learning. The proposed model was verified by a dataset collected from social network services and outperformed baselines.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.117-133
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• 2021

This study aimed to identify its research trends using social network analysis(SNA). The results of the analysis showed that, for degree centrality, Busan Port(0.223) was the keyword that had the highest centrality, followed by DEA(0.060), AHP(0.056), and container terminal and port competitiveness(0.049). Busan Port(0.245) also had the highest betweenness centrality, followed by DEA(0.048), container terminal(0.044), AHP(0.039), and Busan New Port(0.032). The trend analysis inferred that efficiency analysis(DEA), strategy selection, and competition analysis(AHP) were the keywords with a high centrality for Busan Port to gain a competitive edge with global ports. However, research on the Fourth Industrial Revolution, which is emerging as a key issue, was insufficient. In the future, research using social data, such as mass media and social networks, is necessary.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.1-13
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• 2020

Recently, 80% of big data consists of unstructured text data. In particular, various types of documents are stored in the form of large-scale unstructured documents through social network services (SNS), blogs, news, etc., and the importance of unstructured data is highlighted. As the possibility of using unstructured data increases, various analysis techniques such as text mining have recently appeared. Therefore, in this study, topic modeling technique was applied to the Korea Highway Corporation's voice of customer (VOC) data that includes customer opinions and complaints. Currently, VOC data is divided into the business areas of Korea Expressway Corporation. However, the classified categories are often not accurate, and the ambiguous ones are classified as "other". Therefore, in order to use VOC data for efficient service improvement and the like, a more systematic and efficient classification method of VOC data is required. To this end, this study proposed two approaches, including method using only the latent dirichlet allocation (LDA), the most representative topic modeling technique, and a new method combining the LDA and the word embedding technique, Word2vec. As a result, it was confirmed that the categories of VOC data are relatively well classified when using the new method. Through these results, it is judged that it will be possible to derive the implications of the Korea Expressway Corporation and utilize it for service improvement.