• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.332-337
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• 2023

The purpose of the study was to analyze consumers' perceptions of gamification. Based on the analyzed data, we would like to provide data by systematically organizing the concept, game elements, and mechanisms of gamification. Recently, gamification can be easily found around medical care, corporate marketing, and education. This study collected keywords from social media portal sites Naver, Daum, and Google from 2018 to 2023 using TEXTOM, a social media analysis tool. In this study, data were analyzed using text mining, semantic network analysis, and CONCOR analysis methods. Based on the collected data, we looked at the relevance and clusters related to gamification. The clusters were divided into a total of four clusters: 'Awareness of Gamification', 'Gamification Program', 'Future Technology of Gamification', and 'Use of Gamification'. Through social media analysis, we want to investigate and identify consumers' perceptions of gamification use, and check market and consumer perceptions to make up for the shortcomings. Through this, we intend to develop a plan to utilize gamification.

• 한국융합학회논문지
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• 제8권12호
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• pp.265-274
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• 2017

본 논문은 빅데이터 분석 교육의 문제점을 고찰해 그 개선 방안을 제시한다. 빅데이터의 특성은 V3에서 V5로 진화하고 있다. 이에 빅데이터 분석 교육도 V5를 감안한 데이터 분석 교육이 되어야 한다. 작금 불확실성의 증대는 데이터 분석의 리스크를 증가시키기에 내적 외적 구조화/비구조화 데이터를 비롯해 교란 요인마저 분석할 때 데이터의 신뢰성은 증가될 수 있다. 그리고 평판분석을 활용할 때 범하기 쉬운 오류가 가변성과 불확실성에 대한 상황 인식이다. 가변성의 측면을 고려해, 다양한 변수와 옵션에 의한 불확실성의 상황을 인식하고 대비한 데이터 분석이 이뤄질 때 데이터에 대한 신뢰성과 정확성은 증가할 수 있다. 사회관계망 분석에서 학생들과 일반 연구자들이 주로 활용하는 것이 텍스톰과 노드엑셀의 노드 분석이다. 사화관계망 분석은 매개중심성에 의한 상황 분석을 통해 다크 데이터를 찾아 이상 현상을 감지하고 현 상황을 분석하여 유용한 의미를 얻고 미래를 예측할 수 있어야 한다.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.630-644
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• 2021

Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.

• International Journal of Internet, Broadcasting and Communication
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• 제12권2호
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• pp.113-119
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• 2020

Numerous tourist-related data produced on the Internet contain not only simple tourist information but also diverse ideas and opinions from users. In order to derive meaningful information about tourist sites from such big data, the social network analysis of tourist keywords can identify the frequency of keywords and the relationship between keywords. Thus, it is possible to make recommendations more suitable for users by utilizing the clear recommendation criteria of tourist attractions and the relationship between tourist attractions. In this paper, a recommendation system was designed based on tourist site information through big data social network analysis. Based on user personality information, the types of tourism suitable for users are classified through deep learning and the network analysis among tourist keywords is conducted to identify the relationship between tourist attractions belonging to the type of tourism. Tour information for related tourist attractions shown on SNS and blogs will be recommended through tagging.

• 한국인터넷방송통신학회논문지
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• 제16권6호
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• pp.225-230
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• 2016

최근 네트워크와 인터넷의 발전으로 웹상에 대용량의 데이터가 생겨났으며, 이를 처리하기 위해 빅데이터 기술이라는 패러다임이 생겨났다. 빅데이터 기술은 기존의 정형 데이터뿐만 아니라 소셜 데이터 등 다양한 비정형 데이터를 이용해 다각적이고 정확한 분석을 목표로 연구되고 있다. 그러나 소셜 데이터는 전문성과 객관성을 가지고 있다고 보기는 힘들고 정보의 조작 및 은폐, 왜곡 등의 문제성이 제기되고 있다. 따라서, 본 논문에서는 신뢰성 빅데이터 플랫폼에 대하여 제안하며, 세부 관리자와 모듈에 대하여 설명한다. 본 논문에서 제안하는 신뢰성 빅데이터 플랫폼은 데이터 정제 관리자, 데이터 분석 관리자, 상호 신뢰 관리자, 시각화 관리자, 검색 관리자로 구성되어진다.

• 한국정보시스템학회지:정보시스템연구
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• 제26권4호
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• pp.419-442
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• 2017

Purpose The purpose of this study is to explore the pattern of user response and it's duration time through social media content response analysis. We also analyze the characteristics of content quality factors which are associate with the user response pattern. The analysis results will provide some implications to develop strategies and schematic plans for the operator of regional marketing on the SNS. Design/methodology/approach This study used mixed methods to verify the effects and responses of social media contents on the users who have concerns about regional events such as local festival, cultural events, and city tours etc. Big data analysis was conducted with the quantitative data from regional government SNSs. The data was collected through web crawling in order to analyze the social media contents. We especially analyzed the contents duration time and peak level time. This study also analyzed the characteristics of contents quality factors using expert evaluation data on the social media contents. Finally, we verify the relationship between the contents quality factors and user response types by cross correlation analysis. Findings According to the big data analysis, we could find some content life cycle which can be explained through empirical distribution with peak time pattern and left skewed long tail. The user response patterns are dependent on time and contents quality. In addition, this study confirms that the level of quality of social media content is closely relate to user interaction and response pattern. As a result of the contents response pattern analysis, it is necessary to develop high quality contents design strategy and content posting and propagation tactics. The SNS operators need to develop high quality contents using rich-media technology and active response contents that induce opinion leader on the SNS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.2966-2986
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• 2023

Climate change is a constant threat to human life, and it is important to understand the public perception of this issue. Previous studies examining climate change have been based on limited survey data. In this study, the authors used big data such as news articles and social media data, within which the authors selected specific keywords related to climate change. Using these natural language data, topic modeling was performed for discourse analysis regarding climate change based on various topics. In addition, before applying topic modeling, sentiment analysis was adjusted to discover the differences between discourses on climate change. Through this approach, discourses of positive and negative tendencies were classified. As a result, it was possible to identify the tendency of each document by extracting key words for the classified discourse. This study aims to prove that topic modeling is a useful methodology for exploring discourse on platforms with big data. Moreover, the reliability of the study was increased by performing topic modeling in consideration of objective indicators (i.e., coherence score, perplexity). Theoretically, based on the social amplification of risk framework (SARF), this study demonstrates that the diffusion of the agenda of climate change in public news media leads to personal anxiety and fear on social media.

• 한국의류학회지
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• 제40권6호
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• pp.1034-1044
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• 2016

This study seeks a method to objectively evaluate sensibility based on Big Data in the field of design. In order to do so, this study examined the sensibility responses on design factors for the public through a network analysis of texts displayed in social media. 'Hanbok', a formal clothing that represents Korea, was selected as the subject for the research methodology. We then collected 47,677 keywords related to Hanbok from 12,000 posts on Naver blogs from January $1^{st}$ to December $31^{st}$ 2015 and that analyzed using social matrix (a Big Data analysis software) rather than using previous survey methods. We also derived 56 key-words related to design elements and sensibility responses of Hanbok. Centrality analysis and CONCOR analysis were conducted using Ucinet6. The visualization of the network text analysis allowed the categorization of the main design factors of Hanbok with evaluation terms that mean positive, negative, and neutral sensibility responses. We also derived key evaluation factors for Hanbok as fitting, rationality, trend, and uniqueness. The evaluation terms extracted based on natural language processing technologies of atypical data have validity as a scale for evaluation and are expected to be suitable for utilization in an index for sensibility evaluation that supplements the limits of previous surveys and statistical analysis methods. The network text analysis method used in this study provides new guidelines for the use of Big Data involving sensibility evaluation methods in the field of design.

• 한국멀티미디어학회논문지
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• 제17권6호
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• pp.671-680
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• 2014

Recently, with the remarkable increase of social network services, it is necessary to extract interesting information from lots of data about various individual opinions and preferences on SNS(Social Network Service). The sentiment information can be applied to various fields of society such as politics, public opinions, economics, personal services and entertainments. To extract sentiment information, it is necessary to use processing techniques that store a large amount of SNS data, extract meaningful data from them, and search the sentiment information. This paper proposes an efficient method to extract sentiment information from various unstructured big data on social networks using HDFS(Hadoop Distributed File System) platform and MapReduce functions. In experiments, the proposed method collects and stacks data steadily as the number of data is increased. When the proposed functions are applied to sentiment analysis, the system keeps load balancing and the analysis results are very close to the results of manual work.

• 한국콘텐츠학회논문지
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• 제20권2호
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• pp.240-247
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• 2020

빠르게 변화하는 온라인상의 정보 흐름과 트랜드를 이해하고 IT기술 환경변화에 대응하기 위해서 필요한 선제적 제도 마련을 위한 한 가지 방안으로 빅데이터를 이용하고자 하는 노력이 최근 들어 더욱 가속화 되고 있다. 논문에서는 인공지능 기반의 빅데이터 처리를 통한 이슈 분석 시스템의 개발과 연구를 통해 빅데이터 처리를 위한 새로운 기술의 가능성을 확인하고자 한다. 이를 위해, 고속의 병렬처리가 가능해진 인공신경망을 사용, 의미 추론 및 패턴분석을 위한 처리 기법을 제안하고 구현을 통해 제안하는 방법에 대한 빅데이터 처리의 적합성을 알아본다. 정보보안의 중요성을 감안하여, 인공 신경망을 이용한 이슈 분석 시스템을 최근의 보안 이슈 분석에 활용해봄으로써 제안하는 방식이 실제 빅데이터 처리에 유용하게 활용 될 수 있음을 검증한다. 실험을 통해서 제안된 방식에 대한 다양한 목적의 빅데이터 처리를 위한 기반 기술로의 활용 가능성을 확인한다.