Earticle

본 연구는 대학생의 인공지능 윤리 인식을 파악하여 대학 교양 인공지능 윤리 교육의 방향성을 탐색하고자 한다. 대학생 83명이 총 5개의 인공지능 윤리 토론 주제에 대한 의견을 작성하고, 작성된 텍스트를 기반으로 텍 스트 마이닝 중 언어 네트워크를 이용하여 분석하였다. 분석 결과, 첫째, 인공지능 사회의 미래에 대해서 62.5% 의 학생이 긍정적으로 바라보고 있었다. 둘째, 자율주행 자동차 사고 발생 시 39.2%의 학생이 현재 자율주행 수 준으로는 차량 소유자의 책임으로 생각하였다. 셋째, 인공지능 발전의 역기능으로는 사생활 침해와 기술 악용, 정보편식을 꼽았다. 역기능 최소화 방안으로 인공지능 사용자와 개발자 모두의 윤리 교육이 필요하며 제도적인 준비도 병행되어야 한다고 언급하였다. 넷째, 얼굴 인식 기술이 보편화된 사회에 대해 19.2%의 학생만이 긍정적 인 의견을 나타내었다. 마지막으로 데이터 수집 시 개인정보 이용 동의를 얻은 부분만 활용해야 할 뿐 아니라 도덕적인 기준이 없는 인공지능 활용 방안에 대해 사용자와 개발자 모두의 윤리적 소양을 강조하였다. 본 연구 는 대학 교양 수준의 인공지능 윤리 교육을 설계할 때 시사점을 제공한다는 점에서 의의가 있다.

In this study, we examine the AI ethics perception of university students to explore the direction of AI ethics education. For this, 83 students wrote their thoughts about 5 discussion topics on online bulletin board. We analyzed it using language networks, one of the text mining techniques. As a result, 62.5% of students spoke the future of the AI society positively. Second, if there is a self-driving car accident, 39.2% of students thought it is the vehicle owner’s responsibility at the current level of autonomous driving. Third, invasion of privacy, abuse of technology, and unbalanced information acquisition were cited as dysfunctions of the development of AI. It was mentioned that ethical education for both AI users and developers is required as a way to minimize malfunctions, and institutional preparations should be carried out in parallel. Fourth, only 19.2% of students showed a positive opinion about a society where face recognition technology is universal. Finally, there was a common opinion that when collecting data including personal information, only the part with the consent should be used. Regarding the use of AI without moral standards, they emphasized the ethical literacy of both users and developers. This study is meaningful in that it provides information necessary to design the contents of artificial intelligence ethics education in liberal arts education.

예루살렘 지역은 종교, 지정학적 위치, 현대의 글로벌 다문화/다인종 사회와 상반되는 역사적/문화적 특 성을 갖는 특수한 공간으로 해외지역학 분야의 연구 가치가 매우 높은 지역임에도 불구하고 국내 예루살렘 지역 연구 분야에서는 특정 주제의 연구로 편중되는 경향을 보인다. 이를 검증하기 위해 본 연구에서는 텍스트마이닝 기반의 토픽모델링과 네트워크 분석 기법을 통해 국내 예루살렘 연구동향을 분석했다. 분석 결과 국내 예루살렘 지역연구는 종교적 차원의 주제로 접근하는 연구로 편중되어 있는 것을 확인했다. 세부적으로 토픽모델링 분석 결 과 ‘기록물’, ‘기독교’, ‘성경’, ‘선교’ 의 네 개 단어를 중심으로 키워드 집단이 추출되었고 네트워크 분석 결과 ‘예 루살렘’, ‘교회’, ‘연구’, ‘의미’, ‘신학’ 키워드에 대한 ‘연결중심성’, ‘매개중심성’, ‘근접중심성’이 높게 분석됐다. 분석 결과를 바탕으로 국내 예루살렘 지역연구 주제의 다각적인 확장을 위해 역사 해석 관점의 학문분야인 ‘문명교류학’ 분야를 제시했다.

Although the Jerusalem area is an area with high research value in the field of overseas area studies, research tends to be concentrated on a specific topic in Korea. In order to verify this, this study analyzed the research trends Jerusalem in Korea through text mining-based topic modeling and network analysis. As a result of the analysis, it was confirmed that the study of the Jerusalem area in Korea was biased toward research approaching the topic from a religious dimension. In detail, as a result of topic modeling analysis, a keyword group was extracted centered on the four words 'Archival Material', 'Christian', 'Bible', and 'Mission Work', and as a result of network analysis, 'Jerusalem', 'Church', 'Research', 'Meaning' ' and 'theology' were analyzed to have ‘Betweenness Centrality’, ‘Degree Centrality’, and ‘Closeness Centrality’.

최근 디지털 사회의 도래로 다양한 데이터가 폭발적으로 증가하고, 그중 문헌 내 주제어를 도출하는 토픽 모델링 에 관한 연구가 활발히 진행되고 있다. 본 논문의 연구목표는 토픽 모델링 방법 중 하나인 DTM(Dynamic Topic Model) 모델을 적용해 D.N.A.(Data, Network, A.I) 분야에 대한 연구동향을 탐색하는데 있다. 실험 데이터는 최근 6년간(2015∼2020) ICT(Information and Communication Technology) 분야 중 기술대분류가 SWㆍAI에 해당하 는 연구과제 1,519개 사업에 대해 DTM 모델을 적용하였다. 실험결과로, D.N.A. 분야의 기술 키워드 Big data, Cloud, Artificial Intelligence와 확장된 의미의 기술 키워드 Unstructured, Edge Computing, Learning, Recognition 등 이 매년 연구에 표출되었으며, 해당 키워드 들이 특정 연구과제에 종속되지 않고 다른 연구과제에서도 포괄적으로 연구 되고 있음을 확인하였다. 끝으로 본 논문의 연구결과는 향후 D.N.A. 분야에 대한 정책기획ㆍ과제기획 등 연구개발 기획 과정과 기업의 기술 확보전략ㆍ마케팅 전략 등 다양한 곳에 활용될 수 있을 것으로 기대한다.

The Topic Modeling research, the methodology for deduction keyword within literature, has become active with the explosion of data from digital society transition. The research objective is to investigate research trends in D.N.A.(Data, Network, Artificial Intelligence) field using DTM(Dynamic Topic Model). DTM model was applied to the 1,519 of research projects with SWㆍA.I technology classifications among ICT(Information and Communication Technology) field projects between 6 years(2015∼2020). As a result, technology keyword for D.N.A. field; Big data, Cloud, Artificial Intelligence, extended keyword; Unstructured, Edge Computing, Learning, Recognition was appeared every year, and accordingly that the above technology is being researched inclusively from other projects can be inferred. Finally, it is expected that the result from this paper become useful for future policyㆍR&D planning and corporation’s technologyㆍmarketing strategy.

Purpose: This study aimed to identify the main keywords, network structures, and topical themes in patient safety incident reports using text network analysis. Methods: The study analyzed patient safety incident reports from a general hospital in Seoul, covering a total of 3,576 cases reported over five years, from 2019 to 2023. Unstructured data were extracted from the text of the incident reports, detailing how the patient safety incidents occurred and how they were managed according to the six-part principles. The analysis was conducted in four steps: 1) word extraction and refinement, 2) keyword extraction and word network generation, 3) network connectivity and centrality analysis, and 4) topic modeling analysis. The NetMiner program was used for data analysis. Results: The analysis of degree, betweenness, and closeness centrality revealed that the most common keywords among the top five were "confirmation," "medication," "inpatient room," "caregiver," and "condition." Topic modeling analysis identified three main topic groups: 1) incidents caused by a lack of awareness of fall risk, 2) incidents of non-compliance with basic medication principles, and 3) incidents due to inaccurate patient identification. Conclusion: To prevent patient safety incidents, it is necessary to promote a culture of safety in hospitals, standardize patient identification procedures, and provide basic training in medication safety and fall prevention to healthcare staff. Furthermore, empirical research on patient safety practices is necessary to encourage active participation in patient safety activities by patients and family caregivers.

Purpose: The purpose of this study was to identify the supportive care needs of parents of preterm children in South Korea using text data from a portal site. Methods: In total, 628 online newspaper articles and 1,966 social network service posts published between January 1 and December 31, 2019 were analyzed. The procedures in this study were conducted in the following order: keyword selection, data collection, morpheme analysis, keyword analysis, and topic modeling. Results: The term "yirundung-yi", which is a native Korean word referring to premature infants, was confirmed to be a useful term for parents. The following four topics were identified as the supportive care needs of parents of preterm children: 1) a vague fear of caring for a baby upon imminent neonatal intensive care unit discharge, 2) real-world difficulties encountered while caring for preterm children, 3) concerns about growth and development problems, and 4) anxiety about possible complications. Conclusion: Supportive care interventions for parents of preterm children should include general parenting methods for babies. A team composed of multidisciplinary experts must support the individual growth and development of preterm children and manage the complications of prematurity using highly accessible media.

CrossRef is an association of scholarly publishers that develops shared infrastructure to support more effective scholarly communications. It is a registration agency for the digital object identifier (DOI), and has built additional services for CrossRef members around the DOI and the bibliographic metadata that publishers deposit in order to register DOIs for their publications. Among these services are CrossCheck, powered by iThenticate, which helps publishers screen for plagiarism in submitted manuscripts and FundRef, which gives publishers standard way to report funding sources for published scholarly research. To add to these services, Cross-Ref launched CrossRef text and data mining services in May 2014. This article will explain the thinking behind CrossRef launching this new service, what it offers to publishers and researchers alike, how publishers can participate in it, and the uptake of the service so far.

COVID-19로 인해 국내외 조직들은 전례없는 상황을 겪고 있으며 이에 대응하기 위한 리더십 연구 역시 부족한 실정이다. 본 연구는 COVID-19 전과 후의 리더십을 데이터로 규명하고자 했다. 이를 위해 한국 기업 조직에서 COVID-19 전과 후로 위기에 요구되는 리더십 행동 차이를 데이터 마이닝 방법을 통해서 규명했다. COVID-19가 팬데믹(Pandamic) 수준으로 선포 되기 이전에 구성원들은 리더들이 내재적/외재적 보상에 더욱 공을 들이고 수평적 조직문화를 만들어주기를 기대한 반면, COVID-19 시대에는 심리적 안전감, 잦은 소통, 미래에 대한 방향성 제시 등을 요구하고 있었다. 이와 같은 위기 상황에서 부각되는 리더십을 본 연구자들은 변혁적 리더십으로 간주하고 두 번째 연구를 설계하여 구성원들의 태도변수인 회복 탄력성과 관계를 실증했다. 회복 탄력성은 위기 상황에서 구성원들이 갖춰야 할 요소로 자주 언급된다. 변혁적 리더십이 COVID-19 시대에 도 회복 탄력성에 유의미하게 영향을 미치는지 살펴봤다. 분석 결과, 변혁적 리더십은 회복 탄력성과 정(+)의 관계를 가지고, 정서적 몰입은 변혁적 리더십과 회복 탄력성의 관계를 부분적으로 매개하였다. 본 연구는 COVID-19 이후로 요구되는 위기 리더십을 데이터로 규명했다는데 실무적으로 기여점을 가지고 있다. 더불어, 위기 상황에서 종종 강조되는 구성원 태도인 회복 탄력성과 관련성을 실증했다는 이론적인 기여점이 있다.

Many organizations are under crisis due to the pandemic of COVID-19, and they may need to understand different leadership during the crisis. This study consisted of two phases. In the first phase, leadership required after COVID-19 was studied by topical modeling which is one of data mining techniques. In the second phase, the primary purpose was to study the correlation between perceived leadership behaviors and personal performance through empirical analysis. Specifically, we analyzed the change in perceived leadership demanded by members of an organization by comparing before and after the declaration of COVID-19. Prior to the declaration, the members frequently mentioned leadership behaviors related to intrinsic and external rewards. After the declaration, on the other hand, words and phrases related to psychological safety, effective communication, and clear direction to the future were frequently observed. Based on the results of the first study, we determined the transformational leadership as the kind of leadership demanded during a crisis like COVID-19, and the second study was designed to test the relationship between perceived transformational leadership and the individual resilience among the members of the organization. In addition, we hypothesized that affective commitment mediates the relationship between the two variables, and the mediation was tested. As a result, we observed a positive relationship between the perceived transformational leadership and the individual resilience, and it was evident that affective commitment partially mediates the relationship. This research contributes to reveal the leadership behaviors demanded in the crisis of COVID-19 by the data mining technique which is important in HR practices. In addition, this research has a theoretical contribution by investigating the leadership style demanded during the time of a crisis and by relating to the individual attitudes. finally, limitations of this research and directions of future research are discussed.

Ik-seon dong is one of the hot place in Seoul especially in these days. This means lots of money and people have been gathering in Ik-seon dong, gentrification was resulted in here. This showed Seoul’s these day’s gentrification, Too fast. Actually Seoul wanted to make detailed design plan in Ik-seon dong, but because of rapid speed, it was really hard work. As a result the plan was made in 2018, but the gentrification of Ik-seon dong give lots of information. In this situation, for analysing the gentrification, proper information, such as land use, the number of visitors, would be needed. However it is really difficult work because there isn’t data base about that. Text-mining is a analysis method using social network, so in this research, there are proper opportunity to reveal Ik-seon dong’s Gentrification.

도덕적인 인간과 정의로운 시민은 도덕과 교육이 지향하는 인간상이 다. 도덕과 교육의 목표로서 두 인간상은 도덕성과 시민성, 도덕교육과 시민교육에 대한 논의의 연속선 상에 있다. 본 연구는 이러한 문제의 식을 바탕으로 도덕적인 인간과 정의로운 시민에 대한 한국 청소년의 인식을 확인하기 위해 진행되었다. 전국의 중·고등학생 942명을 대상 으로, 두 인간상에 대한 학생들의 서술을 텍스트 마이닝 기법을 활용하여 분석하였다. 연구결과에 따르면, 첫째, 두 인간상에 대한 학생들 의 인식은 상당 부분 중첩되어 있었다. 둘째, 도덕적인 인간을 정의하 는 방식에 있어 중학생들은 덕목을 중심으로 서술한 반면, 고등학생들 은 윤리에 대한 심화된 이해를 중심으로 서술하였다. 셋째, 정의로운 시민을 정의하는 방식은 학교 급의 변화에 따라 더 구체적이고 세부적 으로 나타났다. 연구결과를 바탕으로, 본 연구는 도덕과 교육과 도덕과 시민교육을 위한 몇 가지 제안을 한다.

A moral person and a just citizen are the desirable human character of moral education. This study aims to analyze the perception of a moral person and a just citizen. Korean youth's responses to two human characters were analyzed using the text-mining method. According to research, there was an overlapping domain between a moral person and a just citizen. In addition, middle school students described a moral person based on virtues, whereas high school students used the deeper terms of ethics. Finally, the detailed perceptions of a just citizen increased with age. On that basis, this study discusses moral and citizen education.

본 논문은 ‘공공(임대)주택’에 대한 경기도의회 의원들의 입장을 분석한다. 경기도의회(3 대, 1991년)부터 10대(2021년)까지 경기도의회 홈페이지에 등재된 30년의 ‘속기(회의)록 (Text-Big data)’을 대상으로 정당별로 그리고 선수별로 의원 발언을 분류하고 ‘공공(임대) 주택’에 대한 의원 발언이 긍정적인지 부정적인지를 분석하여 발언의 추이와 의원의 입장 차이를 밝힌다. 연구 방법은 빅데이터 분석에 적합한 텍스트마이닝(Text-Mining)을 위해 R 을 사용하여 단어 정제와 빈도 분석을 실시하였다. 또한 발언 내용을 긍정과 부정의 관점 에서 분석하기 위해 내용 분석을 실시하였다. 분석 결과 경기도의회 속기(회의)록에서 '주 택'과 관련한 발언 중 '공공(임대)주택' 발언이 차지하는 비중은 22%로 나타났다. 1. 정당별, 진보와 보수 간의 발언 빈도의 격차는 시기별 역전 현상에도 불구하고 ‘공공(임대)주택’은 보수와 진보 모두 90% 이상 ‘긍정적’인 내용의 발언으로 이루어져 있는 것으로 분석 되었 고 2. 초선 및 재선 이상의 의원들의 ‘공공(임대)주택’에 대한 긍정적인 발언은 90%(52명, 101회)로 의원들 대부분이 긍정적 입장을 나타내었다.

Housing continues to be a social issue, and one of the most important policies to be addressed by central and local governments. As we approach the 30th anniversary of local self-government, it is necessary to examine the position of local councils in the debate over public (rental) housing. However, there are only a few previous studies in this regard, and they are limited to analyzing local governments (such as Seoul Metropolitan Government) rather than local councils. This paper analyzes the stance of members of the Gyeonggi Provincial Assembly on 'public (rental) housing'. Using 30 years of 'shorthand (meeting) logs' (Text-Big data) listed on the Gyeonggi Provincial Assembly website from the 3rd (1991) to the 10th (2021), we classify lawmakers' remarks by party and player, and analyze whether their remarks on 'public (rental) housing' are positive or negative to reveal the trend of remarks and differences in lawmakers' positions. The research method utilized R, a text-mining tool suitable for big data analysis, to analyze the frequency of remarks, and the researcher read and organized the content of the secondary remarks. As a result of the analysis, the proportion of 'public (rental) housing' remarks among remarks related to 'housing' in the shorthand (meeting) record of the Gyeonggi Provincial Assembly was 22%. 1.Despite the disparity in the frequency of remarks between political parties and between liberals and conservatives, the position on 'public (rental) housing' was analyzed as consisting of more than 90% of 'positive' remarks by both conservatives and liberals. 2.The positive remarks on 'public (rental) housing' by first-time and re-elected legislators were 90% (52, 101 times), indicating that most of the legislators expressed a positive position.

As technology becomes integral to customer experiences, the study investigates the adoption of technologies in hotels and their influence on traditional service. The integration of IoT is explored for its potential to create new experiences and increase customer satisfaction, alongside challenges such as security concerns and high investment costs. Therefore, this research gathered Google News data using the keywords "Smart Hotel" and "Hotel IoT" to analyze emerging trends in the hospitality sector. Co-occurrence network analysis and Latent Dirichlet Allocation (LDA) topic modeling unveil key clusters and topics, emphasizing customer experience, technology amenities, and intelligent operation. The findings contribute valuable insights into the evolving landscape of smart hotels and the relationship between IoT and the hospitality sector.

Culture was an always “overloaded” concept during Korea’s colonial period. Like the ideas of literature and art, it was one of the main routes through which Koreans developed a socio-political sense when they were forbidden to speak about politics. Starting in the 1920s especially, Koreans used culture to establish intellectual foundations of modernity, cultivate the masses’ aesthetic senses, and seriously engage with colonial reality. Furthermore, the idea of culture became more complicated in the late 1930s as the colonial government more aggressively employed the cultural idea to propagate a series of wars while mainlining Japan’s ascendency in East Asia. Reflecting upon such a conceptual tug of war by different socio-political actors, this article uses text-mining to explore the changing meanings of culture in a 1930s popular magazine. Run by the proponents of culture as a forefront of social movements, Samch’ŏlli (“Threethousand ri,” which figuratively refers to Korea) was a monthly magazine that lasted for more than a decade from 1929 to 1941, unlike many short-lived journals under censorship. By examining the frequency of the keywords that composed the theme of culture, and the semantic network of culture’s cooccurring words, we diachronically trace the polyphonic meanings of culture in different timeframes. These quantitative and linguistic methods suggest that culture’s semantic network drawn from a 1930s periodical was far larger, more diverse in composition, and more influential than explained in previous studies, especially in its interplay with the various socio-political actors in launching collective projects by Korean intellectuals and the colonial government.

The Environmental, Social, and Governance (ESG) metric is currently the gold standard for assessing how large corporations perform in these three areas of their daily operations. Despite the effectiveness of ESG in analyzing large corporations, little attention has been paid to micro-level research. Investment proposals that include ESG concepts are becoming more common, with major platforms encouraging entrepreneurs to consider these issues in their pitches. As a result, the conventional wisdom regarding these proposals is that the majority of them will result in a higher success rate due to the ESG trend. We investigate whether this is true by analyzing a Kickstarter dataset containing over 9000 online entrepreneurial pitches. To determine which characteristics of these entrepreneurial proposals resulted in increased investment, we used Ordinary Least Squares (OLS) and Logistic Regression. Contrary to popular belief, our findings revealed that using ESG themes in micro-entrepreneurial pitches increased the likelihood of failure..

With the development of deep learning techniques, text mining is producing breakthrough performance improvements, promising future applications, and practical use cases across many fields. Likewise, even though several attempts have been made in the field of financial information, few cases apply the current technological trends. Recently, companies and government agencies have attempted to conduct research and apply text mining in the field of financial information. First, in this study, we investigate various works using text mining to show what studies have been conducted in the financial sector. Second, to broaden the view of financial application, we provide a description of several text mining techniques that can be used in the field of financial information and summarize various paradigms in which these technologies can be applied. Third, we also provide practical cases for applying the latest text mining techniques in the field of financial information to provide more tangible guidance for those who will use text mining techniques in finance. Lastly, we propose potential future research topics in the field of financial information and present the research methods and utilization plans. This study can motivate researchers studying financial issues to use text mining techniques to gain new insights and improve their work from the rich information hidden in text data.

Recently, demand on application or using Bigdata analytics for CRM (Customer Relationship Management) has emerged in industry and academic research. However, most of previous text analytics studies validated algorithms and reported results of analysis without theoretical background or standardized framework. According to this reasons, expanding studies on various contexts and utilization have been limited. This study aims to develop theory-based framework on text mining techniques to evaluate service quality. Hence, previous studies and business cases are reviewed for selecting appropriate algorithms for measuring service quality. In this process, developed framework was applied to analysis customer’s online reviews. This study will be useful initial guideline on business operators who want to evaluate their service quality from user-generated-contents. It also has values on introductory business research on applied text data analysis and expand research scope and method on service research.

본 연구의 목적은 텍스트 마이닝을 활용하여 특허와 뉴스 기사 분석을 통해 블록체인 기술 동향을 탐색하고 사회적 관심을 파악하여 블록체인 정책의제를 제안하는 것이다. 이를 위해 국내 블록체인 특허 요약문 327건과 온라인 뉴스기사 전문 5,941건을 수집하고 전처리 과정을 거쳐 LDA 토픽모델링 방법을 사용하여 특허 토픽 12개와 뉴스 토 픽 19개를 추출하였다. 특허 분석을 통해 인증과 거래 관련 토픽이 높은 비중을 차지하였다. 뉴스 기사 분석 결과, 사회적 관심은 암호화폐에 치중되어 있는 것으로 나타났다. 이러한 분석 결과와 의제설정이론에 근거하여 블록체인 관련 정책의제를 도출하였다. 본 연구는 대용량 텍스트 문서 분석의 자동화된 기법을 활용하여 분석을 효율적·객관적으 로 수행하였으며, 블록체인 기술 동향과 사회적 관심도를 파악한 실증된 기초 분석 자료를 기반으로 정책의제를 제안하 였다. 본 연구에서 제시된 정책의제는 향후 정책 결정과정에의 기초자료로 활용될 수 있을 것이다.

The purpose of this study is to explore the trend of blockchain technology through analysis of patents and news articles using text mining, and to suggest the blockchain policy agenda by grasping social interests. For this purpose, 327 blockchain-related patent abstracts in Korea and 5,941 full-text online news articles were collected and preprocessed. 12 patent topics and 19 news topics were extracted with latent dirichlet allocation topic modeling. Analysis of patents showed that topics related to authentication and transaction accounted were largely predominant. Analysis of news articles showed that social interests are mainly concerned with cryptocurrency. Policy agendas were then derived for blockchain development. This study demonstrates the efficient and objective use of an automated technique for the analysis of large text documents. Additionally, specific policy agendas are proposed in this study which can inform future policy-making processes.

Now is the time for IS scholars to demonstrate the added value of academic theory through its integration with text mining, clearly outline how to implement this for text mining experts outside of the academic field, and move towards establishing this integration as a standard practice. Therefore, in this study we develop a systematic theory-based text-mining framework (TTMF), and illustrate the use and benefits of TTMF by conducting a text-mining project in an actual business case evaluating and improving hotel service quality using a large volume of actual user-generated reviews. A total of 61,304 sentences extracted from actual customer reviews were successfully allocated to SERVQUAL dimensions, and the pragmatic validity of our model was tested by the OLS regression analysis results between the sentiment scores of each SERVQUAL dimension and customer satisfaction (star rates), and showed significant relationships. As a post-hoc analysis, the results of the co-occurrence analysis to define the root causes of positive and negative service quality perceptions and provide action plans to implement improvements were reported.

Big data has a lot of influence around the world. Singapore, EU, United States, and Japan have been trying to find national long-term policies and future issues through Big Data. Korea also established Big Data strategy center to find new growth power. So, we tried to analyze various issue technologies through Big Data analysis methods. Issue technologies are Big Data, 3D printing, Internet of Things (IoT), wearable computing devices (Smart watch and Google glasses) which are introduced by National IT Industry Promotion Agency, Gartner, and SK C&C. We think the end users of technology are public, and SNS is a suitable place to share their thoughts. Otherwise, News uses easy words to understand and delivers the information for public. This study proposes a new approach predicting the future of technologies by simulating human brains: left and right brains. For this this study analyzed SNS data and News data by using text mining and opinion mining. With the sensitivity of SNS and the logicality of News, we found elements of technologies and classified them by positivity and negativity. And then, we did three analyses using Futures Wheel. First, the element analysis of five technologies was conducted. Second, we used these elements to predict the future of technologies. Finally, the possibility of convergence of five technologies was confirmed. This paper has three contributions. First, we found the opportunity and threaten elements of five technologies. Second, we predicted the future of technologies with these elements. Third, we identified the opportunity and threaten elements for the convergence of each technology.