• Journal of Korean Academy of Nursing
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• v.4 no.2
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• pp.1-16
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• 1974

This Study involved the development of a survey form and the collection of data in an effort-to provide information which can be used in the improvement of nursing curricula. The data examined were the kinds courses currently being taught in the curricula of nursing education institutions throughout Korea, credits required for course completion, and year in-which courses are taken. For the purposes of this study, curricula were classified into college, nursing school and vocational school categories. Courses were directed into the 3 major categories of general education courses, supporting science courses and professional education course, and further subdirector as. follows: 1) General education (following the classification of Philip H. phoenix): a) Symbolics, b) Empirics, c) Aesthetics. 4) Synthetics, e) Ethics, f) Synoptic. 2) Supporting science: a) physical science, b) biological science, c) social science, d) behavioral science, e) Health science, f) Educations 3) Professional Education; a) basic courses, b) courses in each of the respective fields of nursing. Ⅰ. General Education aimed at developing the individual as a person and as a member of society is relatively strong in college curricula compared with the other two. a) Courses included in the category of symbolics included Korean language, English, German. Chines. Mathematics. Statics: Economics and Computer most college curricula included 20 credits. of courses in this sub-category, while nursing schools required 12 credits and vocational school 10 units. English ordinarily receives particularly heavy emphasis. b) Research methodology, Domestic affair and women & courtney was included under the category of empirics in the college curricula, nursing and vocational school do not offer this at all. c) Courses classified under aesthetics were physical education, drill, music, recreation and fine arts. Most college curricula had 4 credits in these areas, nursing school provided for 2 credits, and most vocational schools offered 10 units. d) Synoptic included leadership, interpersonal relationship, and communications, Most schools did not offer courses of this nature. e) The category of ethics included citizenship. 2 credits are provided in college curricula, while vocational schools require 4 units. Nursing schools do not offer these courses. f) Courses included under synoptic were Korean history, cultural history, philosophy, Logics, and religion. Most college curricular 5 credits in these areas, nursing schools 4 credits. and vocational schools 2 units. g) Only physical education was given every Year in college curricula and only English was given in nursing schools and vocational schools in every of the curriculum. Most of the other courses were given during the first year of the curriculum. Ⅱ. Supporting science courses are fundamental to the practice and application of nursing theory. a) Physical science course include physics, chemistry and natural science. most colleges and nursing schools provided for 2 credits of physical science courses in their curricula, while most vocational schools did not offer t me. b) Courses included under biological science were anatomy, physiologic, biology and biochemistry. Most college curricula provided for 15 credits of biological science, nursing schools for the most part provided for 11 credits, and most vocational schools provided for 8 units. c) Courses included under social science were sociology and anthropology. Most colleges provided for 1 credit in courses of this category, which most nursing schools provided for 2 creates Most vocational school did not provide courses of this type. d) Courses included under behavioral science were general and clinical psychology, developmental psychology. mental hygiene and guidance. Most schools did not provide for these courses. e) Courses included under health science included pharmacy and pharmacology, microbiology, pathology, nutrition and dietetics, parasitology, and Chinese medicine. Most college curricula provided for 11 credits, while most nursing schools provide for 12 credits, most part provided 20 units of medical courses. f) Courses included under education included educational psychology, principles of education, philosophy of education, history of education, social education, educational evaluation, educational curricula, class management, guidance techniques and school & community. Host college softer 3 credits in courses in this category, while nursing schools provide 8 credits and vocational schools provide for 6 units, 50% of the colleges prepare these students to qualify as regular teachers of the second level, while 91% of the nursing schools and 60% of the vocational schools prepare their of the vocational schools prepare their students to qualify as school nurse. g) The majority of colleges start supporting science courses in the first year and complete them by the second year. Nursing schools and vocational schools usually complete them in the first year. Ⅲ. Professional Education courses are designed to develop professional nursing knowledge, attitudes and skills in the students. a) Basic courses include social nursing, nursing ethics, history of nursing professional control, nursing administration, social medicine, social welfare, introductory nursing, advanced nursing, medical regulations, efficient nursing, nursing english and basic nursing, College curricula devoted 13 credits to these subjects, nursing schools 14 credits, and vocational schools 26 units indicating a severe difference in the scope of education provided. b) There was noticeable tendency for the colleges to take a unified approach to the branches of nursing. 60% of the schools had courses in public health nursing, 80% in pediatric nursing, 60% in obstetric nursing, 90% in psychiatric nursing and 80% in medical-surgical nursing. The greatest number of schools provided 48 crudites in all of these fields combined. in most of the nursing schools, 52 credits were provided for courses divided according to disease. in the vocational schools, unified courses are provided in public health nursing, child nursing, maternal nursing, psychiatric nursing and adult nursing. In addition, one unit is provided for one hour a week of practice. The total number of units provided in the greatest number of vocational schools is thus Ⅲ units double the number provided in nursing schools and colleges. c) In th leges, the second year is devoted mainly to basic nursing courses, while the third and fourth years are used for advanced nursing courses. In nursing schools and vocational schools, the first year deals primarily with basic nursing and the second and third years are used to cover advanced nursing courses. The study yielded the following conclusions. 1. Instructional goals should be established for each courses in line with the idea of nursing, and curriculum improvements should be made accordingly. 2. Course that fall under the synthetics category should be strengthened and ways should be sought to develop the ability to cooperate with those who work for human welfare and health. 3. The ability to solve problems on the basis of scientific principles and knowledge and understanding of man society should be fostered through a strengthening of courses dealing with physical sciences, social sciences and behavioral sciences and redistribution of courses emphasizing biological and health sciences. 4. There should be more balanced curricula with less emphasis on courses in the major There is a need to establish courses necessary for the individual nurse by doing away with courses centered around specific diseases and combining them in unified courses. In addition it is possible to develop skill in dealing with people by using the social setting in comprehensive training. The most efficient ratio of the study experience should be studied to provide more effective, interesting education Elective course should be initiated to insure a man flexible, responsive educational program. 5. The curriculum stipulated in the education law should be examined.

• Korean Journal of Crystallography
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• v.8 no.1
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• pp.6-14
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• 1997

The crystal structures of $Sr_{31}K_{30}-X\;(Sr_{31}K_{30}Si_{100}A1_{92}O_{384};\;a=25.169(5) {\AA}$) and $Sr_{8.5}Tl_{75}-X (Sr_{8.5}Tl_{75}Si_{100}A1_{92}O_{384};\;a=25.041(5) {\AA}$) have been determined by single-crystal X-ray diffraction techniques in the cubic space group $\=F{d3}\;at\;21(1)^{\circ}C$. Each crystal was prepared by ion exchange in a flowing stream of aqueous $Sr(ClO_4)_2\;and\;(K\;or\;T1)NO_3$ whose mole ratio was 1 : 5 for five days. Vacuum dehydration was done at $360^{\circ}C$ for 2d. Their structures were refined to the final error indices $R_1=0.072\;and\;R_w=0.057$ with 293 reflections, and $R_1= 0.058\;and\;R_w=0.044$ with 351 reflections, for which $I>2{\sigma}(I)$, respectively. In dehydrated $Sr_{31}K_{30}-X,\;all\;Sr^{2+}$ ions and $K^+$ ions are located at five different crystallographic sites. Six-teen $Sr^{2+}$ ions per unit cell are at the centers of the double six-rings (site I), filling that position. The remaining 15 $Sr^{2+}$ ions and 17 $K^+$ ions fill site II in the supercage. These $Sr^{2+}$ and $K^+$ ions are recessed ca $0.45{\AA}\;and\;1.06{\AA}$ into the supercage, respectively, from the plane of three oxygens to which each is bound. ($Sr-O=2.45(1){\AA}\;and\;K-O=2.64(1){\AA}$) Eight $K^+$ ons occupy site III'($K-O=3.09(7){\AA}\;and\;3.11(10){\AA}$) and the remaining five $K^+$ ions occupy another site III'($K-O=2.88(7){\AA}\;and\;2.76(7){\AA}$). In $Sr_{8.5}Tl_{75}-X,\;Sr^{2+}\;and\;Tl^+$ ions also occupy five different crystallographic sites. About 8.5 $Sr^{2+}$ ions are at site I. Fifteen $Tl^+$ ions are at site I' in the sodalite cavities on threefold axes opposite double six-rings: each is $1.68{\AA}$ from the plane of its three oxygens ($T1-O=2.70(2){\AA}$). Together these fill the double six-rings. Another 32 $Tl^+$ ions fill site II opposite single six-rings in the supercage, each being $1.48{\AA}$ from the plane of three oxygens ($T1-O=2.70(1){\AA}$). About 18 $Tl^+$ ions occupy site III in the supercage ($T1-O=2.86(2){\AA}$), and the remaining 10 are found at site III' in the supercage ($T1-O=2.96(4){\AA}$).

• The Korean Journal of Air & Space Law and Policy
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• v.32 no.2
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• pp.101-127
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• 2017

Even with the most advanced aviation technology benefits, aircraft accidents are constantly occurring while air passenger transportation volume is expected to double in the next 15 years. Since it is not possible to secure aviation safety only by the post aircraft accident safety action of accident investigations, it has been recognized and consensus has been formed that proactive and predictive prevention measures are necessary. In this sense, the aviation safety management system (SMS) was introduced in 2008 and has been carried out in earnest since 2011. SMS is a proactive and predictive aircraft accident preventive measure, which is a mechanism to eliminate the fundamental risk factors by approaching organizational factors beyond technological factors and human factors related to aviation safety. The methodology is to collect hazards in all the sites required for aircraft operations, to build a database, to analyze the risks, and through managing risks, to keep the risks acceptable or below. Therefore, the improper implementation of SMS indicates that the aircraft accident prevention is insufficient and it is to be directly connected with the aircraft accident. Reports of duty performance related hazards including their own errors are essential and most important in SMS. Under the policy of just culture for voluntary reporting, the guarantee of information providers' anonymity, non-punishment and non-blame should be basically secured, but to this end, under-reporting is stagnant due to lack of trust in their own organizations. It is necessary for the accountable executive(CEO) and senior management to take a leading role to foster the safety culture initiating from just culture with the safety consciousness, balancing between safety and profit for the organization. Though a Ministry of Land, Infrastructure and Transport's order, "Guidance on SMS Implementation" states the training required for the accountable executive(CEO) and senior management, it is not legally binding. Thus it is suggested that the SMS training completion certificates of accountable executive(CEO) and senior management be included in SMS approval application form that is legally required by "Korea Aviation Safety Program" in addition to other required documents such as a copy of SMS manual. Also, SMS related items are missing in the aircraft accident investigation, so that organizational factors in association with safety culture and risk management are not being investigated. This hinders from preventing future accidents, as the root cause cannot be identified. The Aircraft Accident Investigation Manuals issued by ICAO contain the SMS investigation wheres it is not included in the final report form of Annex 13 to the Convention on International Civil Aviation. In addition, the US National Transportation Safety Board(NTSB) that has been a substantial example of the aircraft accident investigation for the other accident investigation agencies worldwide does not appear to expand the scope of investigation activities further to SMS. For these reasons, it is believed that investigation agencies conducting their investigations under Annex 13 do not include SMS in the investigation items, and the aircraft accident investigators are hardly exposed to SMS investigation methods or techniques. In this respect, it is necessary to include the SMS investigation in the organization and management information of the final report format of Annex 13. In Korea as well, in the same manner, SMS item should be added to the final report format of the Operating Regulation of the Aircraft and Railway Accident Investigation Board. If such legal and institutional improvement methods are complemented, SMS will serve the purpose of aircraft accident prevention effectively and contribute to the improvement of aviation safety in the future.

• Journal of Preventive Medicine and Public Health
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• v.30 no.2 s.57
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• pp.437-461
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• 1997

Because of a significant improvement in the economic situation and development of scientific techniques in Korea during the last 30 years, the life expectancy of the Korean people has lengthened considerably and as a result, the number of the elderly has markedly increased. Such an increase of the number of aged population brought about many social, economic, and medical problems which were never seriously considered before. This study was conducted to assess the trend of medical care utilization and medical expenditure of the elderly. The data of each patient in the study were taken from computer database maintained for administrative purpose by the Korea Medical Insurance Corporation. The study population was 132,670 who were 60 years old or more and registered in Korean Medical Insurance Corporation from 1989 to 1993. The study subjects were predominantly female(56.3%) and 10,000-20,000 Won premium group(50.6%). The following are summaries of findings : The total increase of the number of inpatient cases was 40.5% from 1989 through 1993. The average annual increase was 3.7% in inpatient medical expenditures per case, 4.4% in inpatient medical expenditures per day and 0.08% in length of stay per case from 1989 through 1993. Cataract was the most prevalent disease of 10 leading frequent diseases in all ages from 1989 through 1993. The case mix in 1993 compared to 1989 revealed that cataract and ischemic cerebral disease were increased whereas essential hypertension and pulmonary tuberculosis were decreased . The average annual increase of medical expenditures was 3.8% in general hospitals, 6.3% in hospitals and 2.4% in clinics. From 1989 through 1993, medical expenditures used by high-cost patients accounted for about 14% to 20% of all expenditures for inpatient care, while they represented less than 2.5% of the elderly population. Time series analysis revealed that total medical expenditures and doctor's fee for inpatient will be progressively increased whereas drug expenditures for inpatient will be decreased. And there will be no change in length of stay. Based on the above results, the factors increasing medical cost and utilization should be identified and the method of cost containment for the elderly health care should be developed systematically.

• The Korean Journal of Nuclear Medicine
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• v.38 no.6
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• pp.486-491
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• 2004

Purpose: factor analysis and independent component analysis (ICA) has been used for handling dynamic image sequences. Theoretical advantages of a newly suggested ICA method, ensemble ICA, leaded us to consider applying this method to the analysis of dynamic myocardial $H_2^{15}O$ PET data. In this study, we quantified patients' blood flow using the ensemble ICA method. Materials and Methods: Twenty subjects underwent $H_2^{15}O$ PET scans using ECAT EXACT 47 scanner and myocardial perfusion SPECT using Vertex scanner. After transmission scanning, dynamic emission scans were initiated simultaneously with the injection of $555{\sim}740$ MBq $H_2^{15}O$. Hidden independent components can be extracted from the observed mixed data (PET image) by means of ICA algorithms. Ensemble learning is a variational Bayesian method that provides an analytical approximation to the parameter posterior using a tractable distribution. Variational approximation forms a lower bound on the ensemble likelihood and the maximization of the lower bound is achieved through minimizing the Kullback-Leibler divergence between the true posterior and the variational posterior. In this study, posterior pdf was approximated by a rectified Gaussian distribution to incorporate non-negativity constraint, which is suitable to dynamic images in nuclear medicine. Blood flow was measured in 9 regions - apex, four areas in mid wall, and four areas in base wall. Myocardial perfusion SPECT score and angiography results were compared with the regional blood flow. Results: Major cardiac components were separated successfully by the ensemble ICA method and blood flow could be estimated in 15 among 20 patients. Mean myocardial blood flow was $1.2{\pm}0.40$ ml/min/g in rest, $1.85{\pm}1.12$ ml/min/g in stress state. Blood flow values obtained by an operator in two different occasion were highly correlated (r=0.99). In myocardium component image, the image contrast between left ventricle and myocardium was 1:2.7 in average. Perfusion reserve was significantly different between the regions with and without stenosis detected by the coronary angiography (P<0.01). In 66 segment with stenosis confirmed by angiography, the segments with reversible perfusion decrease in perfusion SPECT showed lower perfusion reserve values in $H_2^{15}O$ PET. Conclusions: Myocardial blood flow could be estimated using an ICA method with ensemble learning. We suggest that the ensemble ICA incorporating non-negative constraint is a feasible method to handle dynamic image sequence obtained by the nuclear medicine techniques.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.