• Information Systems Review
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• v.19 no.2
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• pp.1-19
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• 2017

Original scripts of e-learning lectures for the CEOs of corporation S were analyzed using topic analysis, which is a text mining method. Twenty-two topics were extracted based on the keywords chosen from five-year records that ranged from 2011 to 2015. Research analysis was then conducted on various issues. Promising topics were selected through evaluation and element analysis of the members of each topic. In management and economics, members demonstrated high satisfaction and interest toward topics in marketing strategy, human resource management, and communication. Philosophy, history of war, and history demonstrated high interest and satisfaction in the field of humanities, whereas mind health showed high interest and satisfaction in the field of in lifestyle. Studies were also conducted to identify topics on the proportion of content, but these studies failed to increase member satisfaction. In the field of IT, educational content responds sensitively to change of the times, but it may not increase the interest and satisfaction of members. The present study found that content production for CEOs should draw out deep implications for value innovation through technology application instead of simply ending the technical aspect of information delivery. Previous studies classified contents superficially based on the name of content program when analyzing the status of content operation. However, text mining can derive deep content and subject classification based on the contents of unstructured data script. This approach can examine current shortages and necessary fields if the service contents of the themes are displayed by year. This study was based on data obtained from influential e-learning companies in Korea. Obtaining practical results was difficult because data were not acquired from portal sites or social networking service. The content of e-learning trends of CEOs were analyzed. Data analysis was also conducted on the intellectual interests of CEOs in each field.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.33-56
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• 2017

As Internet usage is becoming more common worldwide and smartphone become necessity in daily life, technologies and applications related to mobile Internet are developing rapidly. The results of the Internet usage patterns of consumers around the world imply that there are many potential new business opportunities for mobile Internet technologies and applications. The location-based service (LBS) is a service based on the location information of the mobile device. LBS has recently gotten much attention among many mobile applications and various LBSs are rapidly developing in numerous categories. However, even with the development of LBS related technologies and services, there is still a lack of empirical research on the intention to use LBS. The application of previous researches is limited because they focused on the effect of one particular factor and had not shown the direct relationship on the intention to use LBS. Therefore, this study presents a research model of factors that affect the intention to use and actual use of LBS whose market is expected to grow rapidly, and tested it by conducting a questionnaire survey of 330 users. The results of data analysis showed that service customization, service quality, and personal innovativeness have a positive effect on the intention to use LBS and the intention to use LBS has a positive effect on the actual use of LBS. These results implies that LBS providers can enhance the user's intention to use LBS by offering service customization through the provision of various LBSs based on users' needs, improving information service qualities such as accuracy, timeliness, sensitivity, and reliability, and encouraging personal innovativeness. However, privacy concerns in the context of LBS are not significantly affected by service customization and personal innovativeness and privacy concerns do not significantly affect the intention to use LBS. In fact, the information related to users' location collected by LBS is less sensitive when compared with the information that is used to perform financial transactions. Therefore, such outcomes on privacy concern are revealed. In addition, the advantages of using LBS are more important than the sensitivity of privacy protection to the users who use LBS than to the users who use information systems such as electronic commerce that involves financial transactions. Therefore, LBS are recommended to be treated differently from other information systems. This study is significant in the theoretical point of contribution that it proposed factors affecting the intention to use LBS in a multi-faceted perspective, proved the proposed research model empirically, brought new insights on LBS, and broadens understanding of the intention to use and actual use of LBS. Also, the empirical results of the customization of LBS affecting the user's intention to use the LBS suggest that the provision of customized LBS services based on the usage data analysis through utilizing technologies such as artificial intelligence can enhance the user's intention to use. In a practical point of view, the results of this study are expected to help LBS providers to develop a competitive strategy for responding to LBS users effectively and lead to the LBS market grows. We expect that there will be differences in using LBSs depending on some factors such as types of LBS, whether it is free of charge or not, privacy policies related to LBS, the levels of reliability related application and technology, the frequency of use, etc. Therefore, if we can make comparative studies with those factors, it will contribute to the development of the research areas of LBS. We hope this study can inspire many researchers and initiate many great researches in LBS fields.

• The Korean Journal of Nuclear Medicine Technology
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• v.12 no.3
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• pp.208-213
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• 2008

Purpose: Generally a whole body bone scan has been known as one of the most frequently executed exams in the nuclear medicine fields. Asan medical center, usually use various gamma camera systems - manufactured by PHILIPS (PRECEDENCE, BRIGHTVIEW), SIEMENS (ECAM, ECAM signature, ECAM plus, SYMBIA T2), GE (INFINIA) - to execute whole body scan. But, as we know, each camera's sensitivity is not same so it is hard to consistent diagnosis of patients. So our purpose is when we execute whole body bone scans, we exclude uncontrollable factors and try to correct controllable factors such as inherent sensitivity of gamma camera. In this study, we're going to measure each gamma camera's sensitivity and study about reasonable correction factors of whole body bone scan to follow up patient's condition using different gamma cameras. Materials and Methods: We used the $^{99m}Tc$ flood phantom, it recommend by IAEA recommendation based on general counts rate of a whole body scan and measured counts rates by the use of various gamma cameras - PRECEDENCE, BRIGHTVIEW, ECAM, ECAM signature, ECAM plus, IFINIA - in Asan medical center nuclear medicine department. For measuring sensitivity, all gamma camera equipped LEHR collimator (Low Energy High Resolution multi parallel Collimator) and the $^{99m}Tc$ gamma spectrum was adjusted around 15% window level, the photo peak was set to 140-kev and acquirded for 60 sec and 120 sec in all gamma cameras. In order to verify whether can apply calculated correction factors to whole body bone scan or not, we actually conducted the whole body bone scan to 27 patients and we compared it analyzed that results. Results: After experimenting using $^{99m}Tc$ flood phantom, sensitivity of ECAM plus was highest and other sensitivity order of all gamma camera is ECAM signature, SYMBIA T2, ECAM, BRIGHTVIEW, IFINIA, PRECEDENCE. And yield sensitivity correction factor show each gamma camera's relative sensitivity ratio by yielded based on ECAM's sensitivity. (ECAM plus 1.07, ECAM signature 1.05, SYMBIA T2 1.03, ECAM 1.00, BRIGHTVIEW 0.90, INFINIA 0.83, PRECEDENCE 0.72) When analyzing the correction factor yielded by $^{99m}Tc$ experiment and another correction factor yielded by whole body bone scan, it shows statistically insignificant value (p<0.05) in whole body bone scan diagnosis. Conclusion: In diagnosing the bone metastasis of patients undergoing cancer, whole body bone scan has been conducted as follow up tests due to its good points (high sensitivity, non invasive, easily conducted). But as a follow up study, it's hard to perform whole body bone scan continuously using same gamma camera. If we use same gamma camera to patients, we have to consider effectiveness of equipment's change by time elapsed. So we expect that applying sensitivity correction factor to patients who tested whole body bone scan regularly will add consistence in diagnosis of patients.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.137-152
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• 2012

In the generation of Web 2.0, as many users start to make lots of web contents called user created contents by themselves, the World Wide Web is overflowing by countless information. Therefore, it becomes the key to find out meaningful information among lots of resources. Nowadays, the information retrieval is the most important thing throughout the whole field and several types of search services are developed and widely used in various fields to retrieve information that user really wants. Especially, the legal information search is one of the indispensable services in order to provide people with their convenience through searching the law necessary to their present situation as a channel getting knowledge about it. The Office of Legislation in Korea provides the Korean Law Information portal service to search the law information such as legislation, administrative rule, and judicial precedent from 2009, so people can conveniently find information related to the law. However, this service has limitation because the recent technology for search engine basically returns documents depending on whether the query is included in it or not as a search result. Therefore, it is really difficult to retrieve information related the law for general users who are not familiar with legal terms in the search engine using simple matching of keywords in spite of those kinds of efforts of the Office of Legislation in Korea, because there is a huge divergence between everyday words and legal terms which are especially from Chinese words. Generally, people try to access the law information using everyday words, so they have a difficulty to get the result that they exactly want. In this paper, we propose a term mapping methodology between everyday words and legal terms for general users who don't have sufficient background about legal terms, and we develop a search service that can provide the search results of law information from everyday words. This will be able to search the law information accurately without the knowledge of legal terminology. In other words, our research goal is to make a law information search system that general users are able to retrieval the law information with everyday words. First, this paper takes advantage of tags of internet blogs using the concept for collective intelligence to find out the term mapping relationship between everyday words and legal terms. In order to achieve our goal, we collect tags related to an everyday word from web blog posts. Generally, people add a non-hierarchical keyword or term like a synonym, especially called tag, in order to describe, classify, and manage their posts when they make any post in the internet blog. Second, the collected tags are clustered through the cluster analysis method, K-means. Then, we find a mapping relationship between an everyday word and a legal term using our estimation measure to select the fittest one that can match with an everyday word. Selected legal terms are given the definite relationship, and the relations between everyday words and legal terms are described using SKOS that is an ontology to describe the knowledge related to thesauri, classification schemes, taxonomies, and subject-heading. Thus, based on proposed mapping and searching methodologies, our legal information search system finds out a legal term mapped with user query and retrieves law information using a matched legal term, if users try to retrieve law information using an everyday word. Therefore, from our research, users can get exact results even if they do not have the knowledge related to legal terms. As a result of our research, we expect that general users who don't have professional legal background can conveniently and efficiently retrieve the legal information using everyday words.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.19-31
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• 2021

This study tried to identify the characteristics of college students' entrepreneurial intentions in the entertainment industry, focusing on existing literature studies. Based on this, it was intended to suggest realistic educational alternatives for university student start-ups and implications for start-up management to university start-up officials and those in charge of national start-up support policy. Therefore, the implications of this study are as follows. First, technology(item) for idea creation education, which is an essential element in the entertainment industry, how to connect ideas and products, technology methods that can increase content value, and user characteristics education within the entertainment industry will need to be continued. In addition, along with the idea education, it is necessary to increase the understanding of start-up business management such as financing, human resource management, marketing, and operation management, and furthermore, confidence education should be provided so that the possibility of success in an entertainment start-up and a sense of adventure in a new job can be developed. Second, the space and equipment necessary for start-up (club room, student start-up room, entertainment-related equipment, etc.) should be provided centering on the opinion survey of students who are interested in starting a business, and various regulations of universities and government for student start-up should be relaxed. will have to In addition, education for the formation of entrepreneurial knowledge inside and outside of the school, special lectures and consultations by experts, and on-the-spot education, etc., should be made to create more practical entrepreneurial knowledge. something to do. Third, for students wishing to start a business in the entertainment industry, it is necessary to inform their families about the field situation of the entertainment industry accurately so that their children can develop a positive perception rather than a negative perception when choosing a business field. In addition, by promoting various successful cases of college students to their families after starting a business, families should be encouraged so that their children can develop a challenging spirit about starting a business. Fourth, it should be possible to form continuous clubs or gatherings with friends who wish to start a business in the entertainment industry, and furthermore, an opportunity to listen to the opinions of friends who actually started a business through these meetings should be provided. In addition, the meeting and the formation of friends should create a place for discussion about writing a business plan, how to succeed in starting a business, and management of startups, and psychological stimulation activities should be conducted so that each other's will to start a business arises. Fifth, various knowledge related to start-up (methods for securing funds, management of start-up organizations, grasping information about the market in which they want to start a business, etc.) should be cultivated, and how to write a business plan for the various entertainment industry fields they want to start up. You will also need to train them to be practical. Also, based on this knowledge formation, students themselves should be able to respond to risks and changes that may occur in entrepreneurship. Lastly, it is necessary to increase the understanding of business start-up management, and various psychological stimulation activities are needed to make the confidence and fear of starting a business disappear.

• Journal of the Korean association of regional geographers
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• v.3 no.2
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• pp.37-87
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• 1997

As a results of researches on the cultivation processes and settlement developments on the Mangyoung river valley as a whole could be have four 'Space-Time Continuity' through a [Origin-Destination] theory model. On a initial phases of cultivation, the cultivation process has been begun at mountain slopes and tributory plains in upper part of river-basin from Koryo Dynasty to early Chosun Dynasty. At first, indigenous peasants burned forests on the mountain slopes for making 'dryfield' for a cereal crops. Following population increase more stable food supply is necessary facets of life inducing a change production method into a 'wetfield' in tributory plains matching the population increase. First sedentary agriculture maybe initiated at this mountain slopes and tributory plains on upper part of river basin through a burning cultivation methods. Mountain slopes and tributory plains are become a Origin area in cultivation processes. It expanded from up to down through the valleys with 'a bits of land' fashion in a steady pace like a terraced fields expanded with bit by bit of land to downward. They expanded their land to the middle part of river basin in mid period of Chosun Dynasty with dike construction techniques on the river bank. Lower part of river cultivated with embankment building techniques in 1920s and then naturally expanded to the tidal marshes on the estuaries and river inlets of coastal areas. 'Pioneer fringes' are consolidated at there in modern times. Changes in landscapes are appeared it's own characters with each periods of time. Followings are results of study through the Mangyoung river valley as a whole. (1) Mountain slopes and tributory plains on the upper part of river are cultivated 'dryfields' by indigenous peasants with Burning cultivation methods at first and developed sedentary settlements at the edges of mountain slopes and on the river terrace near the fields. They formed a kind of 'periphery-located cluster type' of settlement. This type of settlement are become a prominant type in upper part of river basin. 'Dryfields' has been changed into a 'wetfields' at the narrow tributory plains by increasing population pressure in later time. These wetfields are supplied water by Weir and Ponds Irrigation System(제언수리방법). Streams on the tributory plains has been attracted wetfields besides of it and formed a [water+land] complex on it. 'Wetfields' are expanded from up to downward with a terraced land pattern(adder like pattern, 붕전) according to the gradient of valley. These periphery located settlements are formed a intimate ecological linkage with several sets of surroundings. Inner villages are expanded to Outer villages according to the expansion of arable lands into downward. (2) Mountain slopes and tributory plains expanded its territory to the alluvial deposited plains on the middle part of river valley with a urgent need of new land by population increase. This part of alluvial plains are cultivated mainly in mid period of Chosun Dynasty. Irrigation methods are changed into a Dike Construction Irrigation method(천방수리방법) for the control of floods. It has a trend to change the subjectives of cultivation from community-oriented one who constructed Bochang along tributories making rice paddies to local government authorities who could be gather large sums of capitals, techniques and labours for the big dike construction affairs. Settlements are advanced in the midst of plains avoiding friction of distances and formed a 'Centrallocated cluster type' of settlements. There occured a hierarchical structures of settlements in ranks and sizes according merits of water supply and transportation convenience at the broad plains. Big towns are developed at there. It strengthened a more prominant [water+land] complex along the canals. Ecological linkages between settlements and surroundings are shaded out into a tiny one in this area. (3) It is very necessary to get a modern technology of flood control at the rivers that have a large volume of water and broad width. The alluvial plains are remained in a wilderness phase until a technical level reached a large artificial levee construction ability that could protect the arable land from flood. Until that time on most of alluvial land at the lower part of river are remained a wilderness of overgrown with reeds in lacks of techniques to build a large-scale artificial levee along the riverbank. Cultivation processes are progressed in a large scale one by Japanese agricultural companies with [River Rennovation Project] of central government in 1920s. Large scale artificial levees are constructed along the riverbank. Subjectives of cultivation are changed from Korean peasants to Japanese agricultural companies and Korean peasants fell down as a tenant in a colonial situation of that time in Korea. They could not have any voices in planning of spatial structure and decreased their role in planning. Newly cultivated lands are reflected company's intensions, objectives and perspectives for achieving their goals for the sake of colonial power. Newly cultivated lands are planned into a regular Rectangular Block settings of rice paddies and implanted a large scale Bureaucratic-oriented Irrigation System on the cultivated plains. Every settlements are located in the midst of rice paddies with a Central located Cluster type of settlements. [water+land] complex along the canal system are more strengthened. Cultivated space has a characters of [I-IT] landscapes. (4) Artificial levees are connected into a coastal emnankment for a reclamation of broad tidal marshes on the estuaries and inlets of rivers in the colonial times. Subjectives of reclamation are enlarged into a big agricultural companies that could be acted a role as a big cultivator. After that time on most of reclamation project of tidal marshes are controlled by these agricultural companies formed by mostly Japanese capitalists. Reclaimed lands on the estuaries and river inlets are under hands of agricultural companies and all the spatial structures are formed by their intensions, objectives and perspectives. They constructed a Unit Farming Area for the sake of companies. Spatial structures are planned in a regular one with broad arable land for the rice production of rectangular blocks, regular canal systems and tank reservoir for the irrigation water supply into reclaimed lands. There developed a 'Central-located linear type' of settlements in midst of reclaimed land. These settlements are settled in a detail program upon this newly reclaimed land at once with a master plan and they have planned patterns in their distribution, building materials, location, and form. Ecological linkage between Newly settled settlemrnts and its surroundings are lost its colours and became a more artificial one by human-centred environment. [I-IT] landscapes are become more prominant. This region is a destination area of [Origin-Destination] theory model and formed a 'Pioneer Fringe'. It is a kind of pioneer front that could advance or retreat discontinously by physical conditions and socio-cultural conditions of that region.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.