• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.207-210
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• 2018

인플루언서(Influencer)란 소셜 미디어에서 유난히 많은 영향력과 파급효과를 가지고 오는 사람들을 말하며, 이들이 만들어내는 콘텐츠는 이제는 자신들의 브랜딩을 넘어선 커머스(Commerce) 효과를 발휘하고 있다. 본 논문에서는 소셜 웹 그리고 공공데이터를 중심으로 뷰티 빅데이터와 방송 콘텐츠 빅데이터를 수집하고 분석하여 상호 상관성에 기반하여 화장품 관련 기업에서 CRM(Customer Relation Management), PLM(Product Lifecycle Management, SCM(Supply Chain Management System) 등의 경영정보시스템과 연계한 뷰티 분야에 최적화된 통합 경영정보시스템을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.914-917
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• 2019

SNS 의 발전으로 기업이나 공공단체는 소셜 데이터가 가지고 있는 감성이나 의견, 여론 등을 분석해서 신흥 가치를 창출하려 한다. 소셜 데이터를 기반으로 하는 감성 분석은 사람들의 소비 측면 및 제품 평가 파악은 물론 기업 매출 및 정책 수립 등에서 도움이 된다. 하지만 소셜 데이터는 각종 신조어 및 이모티콘이 다수 포함되어 있어 기존 감성 분석 방법으로는 정확한 분석을 하기 어렵다. 이러한 문제를 해결하기 위해 본 논문에서는 신조어 및 이모티콘 감성 사전을 구축하고, 분석 과정에서 기존 감성 사전과 본 논문에서 구축된 신조어 및 이모티콘 감성 사전을 사용하여 감성 분석 정확도를 비교한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.682-683
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• 2014

최근 IT업체들은 온라인 상에서 소비자들이 평소에 쏟아내는 의견들을 수집, 축적해서, 원하는 키워드를 중심으로 내용을 분석함으로써, 특정 주제에 대해 어떤 여론이 형성되고 있으며, 여론이 어떻게 전파되고 있는지 경로를 파악할 수 있는 소셜 빅데이터 분석 툴을 경쟁적으로 개발하고 있다. 본 논문에서는 소셜 빅 데이터를 분석함에 있어 이슈를 감지하고 예측하는 기술을 실제 사례에 적용하여 분석한 결과를 고찰해 보고자 한다. 소셜 미디어 데이터 패턴을 비교 분석하고 부정이슈 감지를 위해 부정 여론을 확산시키는데 영향을 미치는 내용과 작성자를 독립변수로 하고, 평균 이슈 도달 시간 및 속도를 종속변수로 정의한다. 부정 여론 형성의 영향력은 트윗수, 리트윗 수를 기준으로 이슈 감지한다. 분석결과 전체 트윗 중 리트윗 메시지가 큰 비중 차지하고 이슈에 대한 버즈가 증가할수록 리트윗 비중이 증가하였으며 크게 확산될 때는 리트윗량이 크게 증가하여 짧은 시간 안에 넓게 확산하였다.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.96-104
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• 2020

As consumers' consumption activities become more active due to the activation of online shopping malls, companies are conducting item trend analyses to boost sales. The existing item trend analysis methods are analyzed by considering only the activities of users in online shopping mall services, making it difficult to identify trends for new items without purchasing history. In this paper, we propose a trend analysis method that combines data in online shopping mall services and social network data to analyze item trends in users and potential customers in shopping malls. The proposed method uses the user's activity logs for in-service data and utilizes hot topics through word set extraction from social network data set to reflect potential users' interests. Finally, the item trend change is detected over time by utilizing the item index and the number of mentions in the social network. We show the superiority of the proposed method through performance evaluations using social network data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.447-461
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• 2012

A social network service(SNS) is gaining popularity as a new real-time information sharing mechanism. However, the user's privacy infringement is occurred frequently because the information that is shared through a social network include the private information such as user's identity or lifestyle patterns. To resolve this problem, the research about privacy preserving data sharing in social network are being proceed actively. In this paper, we proposed the efficient scheme for privacy preserving data sharing in social network. The proposed scheme provides an efficient conjunctive keyword search functionality. And, users who granted access right to storage server can store and search data in storage server. Also,, our scheme provide join/revocation functionality suited to the characteristics of a dynamic social network.

• The Journal of Bigdata
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• v.7 no.2
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• pp.103-111
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• 2022

There are many institutions, organizations, and sites related to responding to infectious diseases, but as the pandemic situation such as COVID-19 continues for years, there are many changes in the initial and current aspects, and accordingly, policies and response systems are evolving. As a result, regional gaps arise, and various problems are scattered due to trust, distrust, and implementation of policies. Therefore, in the process of analyzing social data including information transmission, Twitter data, one of the major social media platforms containing inaccurate information from unknown sources, was developed to prevent facts in advance. Based on social data, which is unstructured data, an algorithm that can automatically detect infectious disease threats is developed to create an objective basis for responding to the infectious disease crisis to solidify international competitiveness in related fields.

• Journal of KIISE
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• v.43 no.1
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• pp.106-118
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• 2016

Recently, as the generation and sharing of web data have increased, the importance of a social semantic web that combines the semantic web and the social web has also been increasing. In this paper, we propose a trust evaluation scheme based on provenance by extending the PROV model in the social semantic web environment. The proposed scheme manages the provenance of web data and adds the necessary elements for trust evaluation in the PROV model of W3C. The extended PROV model supports data management and provenance tracing. The proposed trust evaluation scheme considers various parameters such as user trust, original data trust, and user evaluation. The evaluated trust is managed as provenance. When processing a query, the proposed scheme generates a result by considering the trust. Therefore, the proposed scheme can manage the provenance of web data and compute data trust correctly by using such various parameters. The evaluated trust becomes a criterion to determine whether the query result can be trusted or not. In order to show the validity of the proposed scheme, we verify its performance using SPARQL queries.

• Journal of the HCI Society of Korea
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• v.12 no.2
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• pp.13-19
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• 2017

Various life-logging based on cloud service have developed social network according to the advanced technology of smartphone and wearable device. Daily digital life on social networks has been shared information and emotion and developed new social relationships. Recent life-logging has required social relationships beyond extension of personal memory and anonymity for privacy protection. This study is to determine social network group by using life-logging data obtained in daily lives and to categorize emotion behavior with anonymity guarantee. Social network group was defined by grouping similar representative emotional behavior. The public's patterns and trends was able to be inferred by analyzing representative emotion and behavior of the social groups network.

• 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.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.591-597
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• 2016

Research question and method play important roles in conducting a research in a scientifically valid way. In today's digitalized research environment, social network service (SNS) has rapidly become a new source of big data. While this shift provides new challenges for researchers in Korea, there is little scholarly discussion of how research questions can be framed and what statistical methods can be applied. This article suggests some basic but primary types of example questions for researchers employing social big data analytics. Further, we illustrate the interface of the intended data set specifically for SNS-mediated communication and information exchange behaviors. Lastly, a statistical test known as proper method for social big data is introduced.