• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.49-56
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• 2014

In this paper, we propose a method for identifying hidden principal sentiments among large scale texts from documents, social data, internet and blogs by analyzing standard language, slangs, argots, abbreviations and emoticons in those words. The IRLBA(Implicitly Restarted Lanczos Bidiagonalization Algorithm) is used for principal component analysis with large scale sparse matrix. The proposed system consists of data acquisition, message analysis, sentiment evaluation, sentiment analysis and integration and result visualization modules. The suggested approaches would help to improve the accuracy and expand the application scope of sentiment analysis in social data.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.727-730
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• 2013

소셜 네트워크 서비스는 사용자간의 통신수단 및 자신을 표현하는 하나의 수단으로 사용되면서 다양한 정보를 보유하고 있다. 이러한 소셜 네트워크 서비스를 모바일 디바이스를 통하여 사용하는 사용자가 늘어 가고 있다. 소셜 네트워크 서비스를 컴퓨터 디바이스를 통하여 사용하는 경우 컴퓨터 디바이스 내부에는 캐쉬, 히스토리와 같은 일반적인 웹 서비스 이용 로그 기록을 남기게 된다. 모바일 디바이스를 사용하여 소셜 네트워크 서비스를 이용하는 경우 원활한 서비스 이용을 위하여 사용자의 개인 정보, 친구 정보, 대화 내용과 같은 유출되면 악용 될 수 있는 민감한 정보를 모바일 디바이스 내부에 저장하여 서비스 한다. 이러한 민감한 데이터는 적절한 보안 관리가 실행되어야 한다. 하지만, 다양한 보안 취약점이 존재한다. 본 논문에서는 이러한 모바일 소셜 네트워크 서비스 데이터 관리 보안 취약점에 대하여 분석하고 대응방안에 대하여 연구한다.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.4
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• pp.91-113
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• 2014

Although social metadata has strengths in creating amount of user-contributed resource descriptions, its function is limited because of its non-systematic characteristics. This research proposed an alternative approach to semantic organization of social metadata. It analyzed the semantics of tags created in LibraryThing in order to provide bibliographic categories for describing information resources. Social information Architecture is adopted in generating the bibliographic categories so that social metadata framework can be constructed. This framework can provide the conceptual foundations for semantically organizing social metadata and is expected to be applied to the existing approaches to automatically organize social metadata.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.153-155
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• 2017

빅데이터 분석은 데이터베이스에 잘 정리된 정형 데이터뿐 아니라 인터넷, 소셜 네트워크 서비스, 모바일 환경에서 생성되는 웹 문서, 이메일, 소셜 데이터 등 비정형 데이터를 효과적으로 분석하는 기술을 말한다. 대부분의 빅데이터 분석 기술 방법들은 기존 통계학과 전산학에서 사용되던 데이터 마이닝, 기계 학습, 자연 언어 처리, 패턴 인식 등이 이에 해당된다. 글로벌 리서치 기관들은 빅데이터를 2011년 이래로 최근 가장 주목받는 신기술로 지목해오고 있다. 따라서 대부분의 산업에서 기업들은 빅데이터의 적용을 통해 가치 창출을 위한 노력을 기하고 있다. 본 연구에서는 다음 커뮤니케이션의 빅데이터 분석도구인 소셜 매트릭스를 활용하여 2017년 5월, 1개월 시점을 설정하고 "4차 산업혁명" 키워드에 대한 소비자들의 인식들을 살펴보았다. 빅데이터 분석의 결과는 다음과 같다. 첫째, 4차 산업혁명 키워드에 대한 연관 검색어 1위는 "후보"가 빈도수(7,613)인 것으로 나타났다. 둘째, 연관 검색어 2위는 "안철수"가 빈도수(7,297), 3위는 "문재인"이 빈도수(5,183)로 각각 나타났다. 다음으로 "4차 산업혁명" 키워드에 대한 검색어 긍정적 여론 빈도수 1위는 새로운(895)으로 나타났고, 부정적 여론 빈도수 1위는 위기(516)가 차지하였다. 이러한 결과 분석결과를 바탕으로 연구의 한계와 시사점을 제시하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.129-130
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• 2018

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Artificial Intelligence" keyword, one month as of May 19, 2018. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Artificial Intelligence" has been found to be technology (4,122). This study suggests theoretical implications based on the results.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.85-92
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• 2019

Companies can identify user groups or consumption trends by collecting and analyzing opinions of many users on special subjects or their products as well as utilize them as various purposes such as predicting some specific trends or marketing strategies. Therefore current analyzing tools of social media have come into use as a means to measure the performances of social media marketing through network's statistical analysis. However these tools require expensive computing and network resources including burden of costs for building up and operating complex software platforms and much operating know-how. Hence, small companies or private business operators have difficulty in utilizing those social media data effectively. This paper proposes a framework applied into developing analysis system of social media. The framework could be set up and operate the system to extract necessary social media's data. Also to design the system, this study suggests a meta-model of proposed framework and to guarantee completeness and consistency, a formal specification of meta-model by using Z language is suggested. Finally, we could verify the clearness of framework's design by performing Z model checking of formal specification's output through Z-EVES tool.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2241-2246
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• 2015

Recently for efficient management, companies have collected and investigated information about customers' feedback by using a system that analyzes big data from social media. This paper proposes more accurate and efficient evaluation method of collecting and investigating customers' feedback using social big data for travel agency, which is representative company of hospitality industry. First, it designs service model and, as a test-bed, analyzes media channel, customer satisfaction, and brand-image etc. of big 5 travel agencies in Korea. In addition, we suggest an analysis result of evaluating preference with positive rate and negative rate by proposed evaluation method. It allows a travel agency to know which area should be improved corresponding to evaluation item; thus, suggested evaluation method is effective to manage customers even more efficiently.

• Proceedings of the Korean Society for Information Management Conference
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• 2012.08a
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• pp.3-6
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• 2012

본 연구의 목적은 도서관 이용자들의 일상생활에서 공간들이 어떠한 관계성을 가지고 이용되는지를 분석하는 것이다. 관계성을 파악하기 위한 데이터로 미국 Puget Sound Region Transportation Department에서 수집한 Household Travel Survey 데이터를 이용하였다. 데이터 분석을 위한 도구로 소셜 네트워크 분석도구 중의 하나인 NodeXL을 사용하였다. 분석결과 선행연구에서와 유사하게 슈퍼마켓, 레스토랑, 쇼핑몰 등이 도서관 이용과 공간적으로 연관성이 있음이 나타났다. 또한 전체 이용자의 분석결과와 비교하여 도서관 이용자만이 가지고 사회적 공간이용의 특성을 발견되었고 도서관 정책개발에 이러한 특성이 반영될 필요가 있음을 제안하였다.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.133-141
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• 2008

Social Web is turning current Web into social platform for knowing people and sharing information. This paper takes major social tagging systems as examples, namely delicious, flickr and youtube, to analyze the social phenomena in the Social Web in order to identify the way of mediating and linking social data. A simple Tag Ontology (TO) is proposed to integrate different social tagging data and mediate and link with other related social metadata. Through several machine learning for tagging data, tag groups and similar user groups are extracted, and then used to learn the tagging ontology. A recommender system adopting the tag ontology is also suggested as an applying field.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.119-128
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• 2010

Recently, social network services are rapidly growing and it is estimated that this trend will continue in the future. Social network data can be published for various purposes such as statistical analysis and population studies. When data publication, however, it may disclose the personal privacy of some people, since it can be combined with external information. Therefore, a social network data holder has to remove the identifiers of persons and modify data which have the potential to disclose the privacy of the persons by combining it with external information. The utility of data is maximized when the modification of data is minimized. In this paper, we propose a privacy protection method for social network data that considers both structural and content information. Previous work did not consider content information in the social network or distorted too much structural information. We also verify the effectiveness and applicability of the proposed method under various experimental conditions.