• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• The Korean Nurse
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• v.28 no.3
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• pp.52-67
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• 1989

True nursing care means total nursing care which includes physical, emotional and spiritual care. The modern nursing care has tendency to focus toward physical care and needs attention toward emotional and spiritual care. The total nursing care is mandatory for patients with terminal cancer and for this purpose, hospice care became emerged. Hospice case originated from the place or shelter for the travellers to Jerusalem in medieval stage. However, the meaning of modem hospice care became changed to total nursing care for dying patients. Modern hospice care has been developed in England, and spreaded to U.S.A. and Canada for the patients with terminal cancer. Nowaday, it became a part of nursing care and the concept of hospice care extended to the palliative care of the cancer patients. Recently, it was introduced to Korea and received attention as model of total nursing care. This study was attempted to assess the efficacy of hospice care. The purpose of this study was to prove a difference in terms of physical, emotional a d spiritual aspect between the group who received hospice care and who didn't receive hospice care. The subject for this study were 113 patients with advanced cancer who were hospitalized in the S different hospitals. 67 patients received hospice care in 4 different hospitals, and 46 patients didn't receive hospice care in another 4 different hospitals. The method of this study was the questionaire which was made through the descriptive study. The descriptive study was made by individual contact with 102 patients cf advanced cancer for 9 months period. The measurement tool for questionaire was made by author through the descriptive study, and included the personal religious orientation obtained from chung(originated R. Fleck) and 5 emotional stages before dying from Kubler Ross. The content ol questionaire consisted in 67 items which included 11 for general characteristics, 10 for related condition with cancer, 13 for wishes far physical therapy, 13 for emotional reactions and 20 for personal religious orientation. Data for this study was collected from Aug. 25 to Oct. 6 by author and 4 other nurse's who received education and training by author for the collection of data. The collected data were ana lysed using descriptive statistics, $X^2-test$, t-test and pearson correlation coefficient. Results of the study were as follows: "H.C Group" means the group of patient with cancer who received hospice care. "Non H.C Group" means the group of patient with cancer who did not receive hospice care. 1. There is a difference between H.C Group and Non H.C Group in term of the number of physical symptoms, subjective degree of pain sensation and pain control, subjective beliefs in physical cure, emotional reaction, help of present emotional and spiritual care from other personal, needs of emotional and spiritual care in future, selection of treatment method by patients and personal religious orientation. 2. The comparison of H.C Group and Non H.C Group 1) There is no difference in wishes for physical therapy between two groups(p=.522). Among Non H.C Group, a group, who didn't receive traditional therapy and herb medicine was higher than a group who received these in degree of belief that the traditional therapy and herb medicine can cure their disease, and this result was higher in comparison to H.C Group(p=.025, p=.050). 2) Non H.C Group was higher than H.C Group in degree of emotional reaction(p=.050). H.C Group was higher than Non H.C Group in denial and acceptant stage among 5 different emotional stages before dying described by Kubler Ross, especially among the patient who had disease more than 13 months(p=.0069, p=.0198). 3) Non H.C Group was higher than H. C Group in demanding more emotional and spiritual care to doctor, nurse, family and pastor(p=. 010). 4) Non H.C Group was higher than H.C Group in demanding more emotional and spiritual care to each individual of doctor, nurse and family (p=.0110, p=.0029, P=. 0053). 5) H.C Group was higher th2.n Non H.C Group in degree of intrinsic behavior orientation and intrinsic belief orientation of personal religious orientation(p=.034, p=.026). 6) In H.C Group and Non H.C Group, the degree of emotional demanding of christians was significantly higher than non christians to doctor, nurse, family and pastor(p=. 000, p=.035). 7) In H.C Group there were significant positive correlations as following; (1) Between the degree of emotional demandings to doctor, nurse, family & pastor and: the degree of intrinsic behavior orientation in personal religious orientation(r=. 5512, p=.000). (2) Between the degree of emotional demandings to doctor, nurse. family & pastor and the degree of intrinsic belief orientation in personal religious orientation(r=.4795, p=.000). (3) Between the degree of intrinsic behavior orientation and the degree of intrinsic: belief orientation in personal religious orientation(r=.8986, p=.000). (4) Between the degree of extrinsic religious orientation and the degree of consensus religious orientation in personal religious orientation (r=. 2640, p=.015). In H.C. Group there were significant negative correlations as following; (1) Between the degree of intrinsic behavior orientation and extrinsic religious orientation in personal religious orientation (r=-.4218, p=.000). (2) Between the degree or intrinsic behavior orientation and consensus religious orientation in personal religious orientation(r=-. 4597, p=.000). (3) Between the degree of intrinsic belief orientations and the degree of extrinsic religious orientation in personal religious orientation(r=-.4388, p=.000). (4) Between the degree of intrinsic belief orientation and the degree of consensus religious orientation in personal religious orientation(r=-. 5424, p=.000). 8) In Non H.C Group there were significant positive correlation as following; (1) Between the degree of emotional demandings to doctor, nurse, family & pastor and the degree of intrinsic behavior orientation in personal religious orientation(r= .3566, p=.007). (2) Between the degree of emotional demandings to doctor, nurse, family & pastor and the degree of intrinsic belief orientation in personal religious orientation(r=.3430, p=.010). (3) Between the degree of intrinsic behavior orientation and the degree of intrinsic belief orientation in personal religious orientation(r=.9723, p=.000). In Non H.C Group there were significant negative correlation as following; (1) Between the degree of emotional demandings to doctor, nurse, family & pastor and the degree of extrinsic religious orientation in personal religious orientation(r= -.2862, p=.027). (2) Between the degree of intrinsic behavior orientation and the degree of extrinsic religious orientation in personal religious orientation(r=-. 5083, p=.000). (3) Between the degree of intrinsic belief orientation and the degree of extrinsic religious orientation in personal religious orientation(r=-. 5013, p=.000). In conclusion above datas suggest that hospice care provide effective total nursing care for the patients with terminal cancer, and hospice care is mandatory in all medical institutions.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of Korean Society of Archives and Records Management
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• v.2 no.2
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• pp.1-38
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• 2002

This study examines the status quo of Korean archives and records management from the Governmental as well as professional activities for the development of the field in relation to the new legislation on records management. Among many concerns, this study primarily explores the following four perspectives: 1) the Government Archives and Records Services; 2) the Korean Association of Archives; 3) the Korean Society of Archives and Records Management; 4) the Journal of Korean Society of Archives and Records Management. One of the primary tasks of the is to build the special depository within which the Presidential Library should be located. As a result, the position of the GARS can be elevated and directed by an official at the level of vice-minister right under a president as a governmental representative of managing the public records. In this manner, GARS can sustain its independency and take custody of public records across government agencies. made efforts in regard to the preservation of paper records, the preservation of digital resources in new media formats, facilities and equipments, education of archivists and continuing, training of practitioners, and policy-making of records preservation. For further development, academia and corporate should cooperate continuously to face with the current problems. has held three international conferences to date. The topics of conferences include respectively: 1) records management and archival education of Korea, Japan, and China; 2) knowledge management and metadata for the fulfillment of archives and information science; and 3) electronic records management and preservation with the understanding of ongoing archival research in the States, Europe, and Asia. The Society continues to play a leading role in both of theory and practice for the development of archival science in Korea. It should also suggest an educational model of archival curricula that fits into the Korean context. The Journals of Records Management & Archives Society of Korea have been published on the six major topics to date. Findings suggest that "Special Archives" on regional or topical collections are desirable because it can house subject holdings on specialty or particular figures in that region. In addition, archival education at the undergraduate level is more desirable for Korean situations where practitioners are strongly needed and professionals with master degrees go to manager positions. Departments of Library and Information Science in universities, therefore, are needed to open archival science major or track at the undergraduate level in order to meet current market demands. The qualification of professional archivists should be moderate as well.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.