• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.133-148
• /
• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Asia pacific journal of information systems
• /
• v.24 no.2
• /
• pp.233-253
• /
• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.35 no.1
• /
• pp.10-24
• /
• 2017

The research takes note of the formation process and settling period of Gwandong-Palkyung(關東八景, Eight Sites of Eastern Korea), the representative palgyeong(prominent eight sites) and jipgyeong(集景, landscape collection of scenic beauty), and investigates the time of formation regarding the palkyung and jipgeyong of Gwandong's scenic beauty through the analysis and interpretation of bibliographic data, and reference data. The result of the study is as follows. As the first document that records the terminology of "Gwandong-Palkyung" is "Daphongeunggil(答洪應吉)" of Yi, Hwang(李滉), Gwandong-Palkyung is inferred to be settled within the recognition of the people even before the 16th century. The geographic analysis result including "Sinjeung Donggukyeojiseungram(新增東國輿地勝覽)", Gwandong-Palkyung expanded as Gwandong-Sipkyung in early to middle of the 16th century. The first confirmed landscape collection regarding Gwandong-Palkyung in this study is confirmed in Shin Zup(申楫)'s "Yeonggwandong-Palkyung(詠關東八景)", thus, the terminology of Gwandong-Palkyung existed before 16th century at the latest. The settlement time of current "Palkyung" collection is estimated to be early 17th century at the latest. Poetries regarding Gwandong-Palkyung, and the frequency on the appearance of Gwandong scenic beauties are analyzed as making clear of the concentrated phenomenon on the sceneries of Gwandong-Palkyung. On the other hand, the collection of Gwandong-Palkyung in the domain of arts is confirmed initially in the ${\ll}$Gwandongpalkyungdobyeong(關東八景圖屛)${\gg}$ of Heo, Pil(許泌). Gwandong-Palkyung, expressed as the actual scene landscape painting shows similar tendencies of the conditions in the jipgyeong from the poetry, but the appearance rate of the painting subject was more prominent in visual solidarity and cohesion due to the reflection of the importance on icon(圖像) of the art works produced with particular meaning in the case of fixed ideal system. From late Joseon to modern times, ${\ll}$palpokbyeongpung(八幅屛風)${\gg}$ of various forms of folk painting is a corroborative evidence notifying that the cultural phenomenon of Gwandong-Palkyung has entered the universal period of embrace. Also, the 13 scenic beauties of Gangwon-Do appearing in the games of Namseungdo and Myeongseungyuramdo include Gwandong-Palkyung, which confirms the settlement of Gwandog-Palkyung even within the culture of games in late Joseon. Such results demonstrate the existence of awareness regarding Gwandong-Palkyung from the first half of the 15th century, which is presumed to have completely settled in the 17th century through the continuous development of formative process in the 16th century. Ultimately, Gwandong-Palkyung is the concrete formation of regional scenic beauties that individually gained its reputations as scenery from the Koryo Dynasty to late 17th century. Gwandong-Palkyung of the scenic beauty of Gwandong is a unique cultural scenery of the region that have germinated and formed through the process of cutting and polishing of long time to collect the best eight of scenic beauty from the many participation of sightseeing culture.

• Journal of Intelligence and Information Systems
• /
• v.19 no.4
• /
• pp.39-53
• /
• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of Intelligence and Information Systems
• /
• v.20 no.3
• /
• pp.93-108
• /
• 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
• /
• v.19 no.1
• /
• pp.95-110
• /
• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Journal of Intelligence and Information Systems
• /
• v.20 no.3
• /
• pp.109-131
• /
• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.34 no.1
• /
• pp.43-50
• /
• 2001

In order to analyze the dynamic motions of fish cage systems made of a frame and a netting under the conditions of waves and current, the hydraulic model experiment at towing tank and the numerical computation using boundary integral element method based on linear potential theory were carried out on a square and a circular type of fish cage, The computed and measured results for the dynamic motions of model fish cage systems showed that the heave and pitch motions were almost unaffected by the inclusion of nets, while the surge motions were very reduced by drag force acting on them. In addition, irregular wave-induced motions of fish cages included non-negligible 2nd order harmonic components at high frequency nearly twice the wave frequency. The reason why these motions were considered was due to resonance or structural components of frames being overflown and out of water during a wave cycle. It was found that circular type was more desirable structure in the open sea than square one only in the respect of dynamic motions due to waves and current. Further verifications were needed considering hydrodynamic forces, fatigue life, and structure analysis based on long term stochastic waves including frequency and time domain for the purpose of analyzing and designing fish cage systems.

• Journal of Korean Academy of Nursing
• /
• v.30 no.6
• /
• pp.1455-1466
• /
• 2000

The purpose of this study was to develop the framework of community-based home care nursing delivery system, and to demonstrate and evaluate the efficiency of it. The study was carned out over a period of 3years from September 1996 to August 1999. The researchers developed Standards for operations, this was all aimed toward a home care recording system, and an assessment intervention algorithm for various diseases quality control and standardization. In the center, 185 patients enrolled, and of the enrollments cerebrovascular disorder and cancer were the most prevailment diseases. Also, a home care nursing activity classification was developed in six domains. Those domains were assessment, medication, treatment, education and consultation, emotional care, and referral or follow-up care. Ten sub-domains were divided according to the systematic needs. Among these nursing activities, treatment, assessment, and education and consultation were frequently performed. In sub-domain classification, skin integrity, respiration, circulation, and immobility related care were provided most frequently. The cost of home care nursing per visit was also suggested. The cost include direct and indirect nursing care, management, and transportation cost. Also, the researchers tried to overcome the limitations of hospital-based home care to provide more accessible, efficient, safe, and stable home care nursing. Therefore, clients were referred from other patients, families, public health care centers, industries, and even hospitals. As a result of this study, several limitations of operation were found. First, it was difficult to manage and communicate with doctor in the emergency situations. Second, there was too much time spent for transportation. This was because they are only five nurses, who cover all of the areas of Seoul and nearby cities. Third, preparation for special care of home care nurses was lacking. Fourth, criteria for the termination of care and the frequency of home visits were ambiguous. Finally, interconnection with home care machinery company was so yely needed. New paragraphs' strategies for solving these problems were suggested. This study will be the basis of community-based home care nursing, and the computerized information delivery system for home care nursing in Korea.

• Journal of electromagnetic engineering and science
• /
• v.16 no.3
• /
• pp.169-181
• /
• 2016

This work presents a new method for enhancing the performance of a dual band Planer Inverted-F Antenna (PIFA) and its lumped equivalent circuit formulation. The performance of a PIFA in terms of return loss, bandwidth, gain, and efficiency is improved with the addition of the proposed open stub in the radiating element of the PIFA without disturbing the operating resonance frequencies of the antenna. In specific cases, various simulated and fabricated PIFA models illustrate that the return loss, bandwidth, gain, and efficiency values of antennas with longer optimum open stub lengths can be enhanced up to 4.6 dB, 17%, 1.8 dBi, and 12.4% respectively, when compared with models that do not have open stubs. The proposed open stub is small and does not interfere with the surrounding active modules; therefore, this method is extremely attractive from a practical implementation point of view. The second presented work is a simple procedure for the development of a lumped equivalent circuit model of a dual band PIFA using the rational approximation of its frequency domain response. In this method, the PIFA's measured frequency response is approximated to a rational function using a vector fitting technique and then electrical circuit parameters are extracted from it. The measured results show good agreement with the electrical circuit results. A correlation study between circuit elements and physical open stub lengths in various antenna models is also discussed in detail; this information could be useful for the enhancement of the performance of a PIFA as well as for its systematic design. The computed radiated power obtained using the electrical model is in agreement with the radiated power results obtained through the full wave electromagnetic simulations of the antenna models. The presented approach offers the advantage of saving computation time for full wave EM simulations. In addition, the electrical circuit depicting almost perfect characteristics for return loss and radiated power can be shared with antenna users without sharing the actual antenna structure in cases involving confidentiality limitations.