• Title, Summary, Keyword: News reports

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J. M. W. Turner's The Shipwreck and the Romantic Semiotics of Maritime Disaster (터너의 <난파선>과 낭만주의적 해양재난)

  • Chun, Dongho
    • The Journal of Art Theory & Practice
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    • no.14
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    • pp.33-51
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    • 2012
  • Joseph Mallord William Turner (1775-1851) has been widely regarded as the most original and brilliant English landscape painter in the 19th century. Admitted to the Royal Academy Schools in 1789, Turner was a precocious artist and gained the full membership of the prestigious Royal Academy in 1802 at the age of 27. Already in the 1800s he was recognised as a pioneer in taking a new and revolutionary approach to the art of landscape painting. Among his early works made in this period, The Shipwreck, painted in 1805, epitomizes the sense of sublime Romanticism in terms of its dramatic subject-matter and the masterly display of technical innovations. Of course, the subject of shipwreck has a long standing history. Ever since human beings first began seafaring, they have been fascinated as much as haunted by shipwrecks. For maritime societies, such as England, shipwreck has been the source of endless nightmares, representing a constant threat not only to individual sailors but also to the nation as a whole. Unsurprisingly, therefore, shipwreck is one of the most popular motifs in art and literature, particularly during the 18th and 19th centuries. Yet accounts, images and metaphors of shipwreck have taken diverse forms and served different purposes, varying significantly across time and between authors. As such, Turner's painting registers a panoply of diverse but interconnected contemporary discourses. First of all, since shipwreck was an everyday occurrence in this period, it is more than likely that Turner's painting depicted the actual sinking in 1805 of the East India Company's ship 'The Earl of Abergavenny' off the coast of Weymouth. 263 souls were lost and the news of the wreck made headlines in major English newspapers at the time. Turner's painting may well have been his visual response to this tragedy, eyewitness accounts of which were given in great quantity in every contemporary newspaper. But the painting is not a documentary visual record of the incident as Turner was not present at the site and newspaper reports were not detailed enough for him to pictorially reconstruct the entire scene. Rather, Turner's painting is indebted to the iconographical tradition of depicting tempest and shipwreck, bearing a strong visual resemblance to some 17th-century Dutch marine paintings with which he was familiar through gallery visits and engravings. Lastly, Turner's Shipwreck is to be located in the contexts of burgeoning contemporary travel literature, especially shipwreck narratives. The late 18th and early 19th century saw a drastic increase in the publication of shipwreck narratives and Turner's painting was inspired by the re-publication in 1804 of William Falconer's enormously successful epic poem of the same title. Thus, in the final analysis, Turner's painting is a splendid signifier leading the beholder to the heart of Romantic abyss conjoing nightmarish everyday experience, high art, and popular literature.

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Comparison Between South and North Korean Terminologies in Child and Family Domains of Family Life Education (남북한 가정생활교육 관련용어 비교분석 - 아동·가족분야를 중심으로 -)

  • Lim, JungHa;Chung, SoonHwa;Song, Jieun
    • Journal of Korean Home Economics Education Association
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    • v.28 no.2
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    • pp.61-78
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    • 2016
  • The purpose of this study was to examine the differences in terminologies used in child and family domains of family life education in South and North Korea. The terminologies relevant to child and family domains in North Korea were selected from various sources including dictionaries that are developed to compare South-North Korean languages, reports and websites by ministry of unification in South Korea, magazines and news articles about North Korean daily life. The collected terminologies were analyzed using the content criteria on core concepts, 'development' and 'relations' from the 2015 proclamation of the ministry of education on home economics curriculum. The major differences between the two Koreas were as follows: first, the terms categorized under human development were more divergent compared to the terms categorized under family relations. Specifically, there were big differences in terminologies in the love and marriage section, the life and labor in pregnancy section in human development and the child caring section in family relations. Second, dissimilar terms were more frequently appeared in content area of kinship, marriage, and child-rearing. Third, the discrepancies of terms between the two Koreas were brought about primarily by differences in political and social system, language refinement, and transcription techniques. These findings including the terminology list would be a practical resources providing for students to familiarize with the differences in the usage of terms and for teachers to develop a home economics educational program in provision of reunification of the Koreas.

An Analysis of Diffusion of Main Information and Peripheral Information: Focusing on Visibility and Connectivity of Word based on Network Analysis (핵심 정보와 주변 정보의 확산 과정 연구: 단어의 가시성(visibility)과 연결성(connectivity) 분석을 중심으로 본 언론의 프레임)

  • Hong, Ju-Hyun
    • The Journal of the Korea Contents Association
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    • v.16 no.3
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    • pp.269-287
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    • 2016
  • This study explores of press report on the death of Beongen Yoo based on network analysis and how issue diffuses via Internet and SNS in mainstream news and conservative channels of comprehensive programming. Issue salience, word's visibility and word's connectivity are the main keyword and analysis criteria of this study. Conservative channel of comprehensive programming focused on the surrounding information rather than core information compared to Mainstream media, Conservative channels of comprehensive media was interested in Yu, Beongeon, an article left, brand, rumor of a body and Mainstream media focused on the results of DNA test. Mainstream media covers this case as the discovery of the Yu, Beongeon body, Mainstream media reported as 'the discovery of the body frame, conservative channels of comprehensive programming reports as blame of investigation at the first stage. The former focuses on the cause of death and the latter focuses on the raising of strong doubts frame at the second stage. In case of the third stage the latter covered on the emphasis of the surrounding information. They frames the issue differently based on network analysis. The view point of conservative channel of comprehensive programming is diffused via SNS. This study highlights the role of journalist of mainstream media in the process of agenda-setting

Stem Cell Governance in Korea After Hwang's affair - Change in Governmental Fiscal Expenditure for R&D Investment - (한국 줄기세포연구정책 거버넌스의 특성 - 황우석 사태 이후 R&D 투자 변화를 중심으로-)

  • Kim, Myungsim
    • Journal of Science and Technology Studies
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    • v.15 no.1
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    • pp.181-214
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    • 2015
  • This study analyzed the characteristics of the politics of technoscience and governance in South Korea, taking advantage of the policy changes on the stem cell research after Hwang's affair. In spite of generally accepted conventional wisdom that stem cell research had been suffering 'crisis' after the Hwang's affair, South Korea succeeded in developing the first and the largest stem cell product in the world. However, considering the fact that the stem cell research capabilities and technological competitiveness of Korea have been assessed as relatively low compared to the development performance, there is a need to extrapolate how such result could be achieved. To answer these questions, we analyzed changes in the R&D expenditure before and after the scandal and verified the 'crisis of stem cell research' following the reduction of financial support from government. From the analysis of literature on the policy reports and news, we described the process of discourse changes in policy and analyzed the characteristics of the politics of technoscience and governance of stem cell research. This study emphasized that the government R&D and regulation policy play the key roles in the development of stem cell research rather than in the technological competitiveness in South Korea. Furthermore, this study argued that democratic governance still does not work under the policy conditions that technocratic decision-making of stem cell research fails to learn from the Hwang's affairs.

An Analysis of IT Trends Using Tweet Data (트윗 데이터를 활용한 IT 트렌드 분석)

  • Yi, Jin Baek;Lee, Choong Kwon;Cha, Kyung Jin
    • Journal of Intelligence and Information Systems
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    • v.21 no.1
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    • pp.143-159
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    • 2015
  • Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

A New Approach to Automatic Keyword Generation Using Inverse Vector Space Model (키워드 자동 생성에 대한 새로운 접근법: 역 벡터공간모델을 이용한 키워드 할당 방법)

  • Cho, Won-Chin;Rho, Sang-Kyu;Yun, Ji-Young Agnes;Park, Jin-Soo
    • Asia pacific journal of information systems
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    • v.21 no.1
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    • pp.103-122
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    • 2011
  • Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

A Study for Factors Influencing the Usage Increase and Decrease of Mobile Data Service: Based on The Two Factor Theory (모바일 데이터 서비스 사용량 증감에 영향을 미치는 요인들에 관한 연구: 이요인 이론(Two Factor Theory)을 바탕으로)

  • Lee, Sang-Hoon;Kim, Il-Kyung;Lee, Ho-Geun;Park, Hyun-Jee
    • Asia pacific journal of information systems
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    • v.17 no.2
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    • pp.97-122
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    • 2007
  • Conventional networking and telecommunications infrastructure characterized by wires, fixed location, and inflexibility is giving way to mobile technologies. Numerous research reports point to the ultimate domination of wireless communication. With the increasing prevalence of advanced cell-phones, various mobile data services (hereafter MDS) are gaining popularity. Although cellular networks were originally introduced for voice communications, statistics indicate that data services are replacing the matured voice service as the growth engine for telecom service providers. For example, SK Telecom, the Korea's largest mobile service provider, reported that 25.6% of revenue and 28.5% of profit came from MDS in 2006 and the share is growing. Statistics also indicate that, in 2006, the average revenue per user (ARPU) for voice didn't change but MDS grew seven percents from the previous year, further highlighting its growth potential. MDS is defined "as an assortment of digital data services that can be accessed using a mobile device over a wide geographic area." A variety of MDS have been deployed, with a few reaching the status of killer applications. Many of them need to access the Internet through the cellular-phone infrastructure. In the past, when the cellular network didn't have acceptable bandwidth for data services, SMS (short messaging service) dominated MDS. Now, Internet-ready, next-generation cell-phones are driving rich digital data services into the fabric of everyday life, These include news on various topics, Internet search, mapping and location-based information, mobile banking and gaming, downloading (i.e., screen savers), multimedia streaming, and various communication services (i.e., email, short messaging, messenger, and chaffing). The huge economic stake MDS has on its stakeholders warrants focused research to understand associated dynamics behind its adoption. Lyytinen and Yoo(2002) pointed out the limitation of traditional adoption models in explaining the rapid diffusion of innovations such as P2P or mobile services. Also, despite the increasing popularity of MDS, unexpected drop in its usage is observed among some people. Intrigued by these observations, an exploratory study was conducted to examine decision factors of MDS usage. Data analysis revealed that the increase and decrease of MDS use was influenced by different forces. The findings of the exploratory study triggered our confirmatory research effort to validate the uni-directionality of studied factors in affecting MDS usage. This differs from extant studies of IS/IT adoption that are largely grounded on the assumption of bi-directionality of explanatory variables in determining the level of dependent variables (i.e., user satisfaction, service usage). The research goal is, therefore, to examine if increase and decrease in the usage of MDS are explained by two separate groups of variables pertaining to information quality and system quality. For this, we investigate following research questions: (1) Does the information quality of MDS increase service usage?; (2) Does the system quality of MDS decrease service usage?; and (3) Does user motivation for subscribing MDS moderate the effect information and system quality have on service usage? The research questions and subsequent analysis are grounded on the two factor theory pioneered by Hertzberg et al(1959). To answer the research questions, in the first, an exploratory study based on 378 survey responses was conducted to learn about important decision factors of MDS usage. It revealed discrepancy between the influencing forces of usage increase and those of usage decrease. Based on the findings from the exploratory study and the two-factor theory, we postulated information quality as the motivator and system quality as the de-motivator (or hygiene) of MDS. Then, a confirmative study was undertaken on their respective role in encouraging and discouraging the usage of mobile data service.

A Study on the Effect of Using Sentiment Lexicon in Opinion Classification (오피니언 분류의 감성사전 활용효과에 대한 연구)

  • Kim, Seungwoo;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.20 no.1
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    • pp.133-148
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    • 2014
  • Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

Text Mining-Based Emerging Trend Analysis for the Aviation Industry (항공산업 미래유망분야 선정을 위한 텍스트 마이닝 기반의 트렌드 분석)

  • Kim, Hyun-Jung;Jo, Nam-Ok;Shin, Kyung-Shik
    • Journal of Intelligence and Information Systems
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    • v.21 no.1
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    • pp.65-82
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    • 2015
  • Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.