• Journal of agricultural medicine and community health
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• v.26 no.1
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• pp.37-56
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• 2001

This study was conducted to analyze the degree of changes in knowledge and attitude toward smoking and to examine the factors affecting knowledge and attitude for smoking after providing a smoking prevention program based on social influence model for a year to middle school students. Study population consists of 665 subjects of middle school students(aged 14 years) in Gumi city in Kyeongsangbukdo Province. Among them three-hundred sixty-seven students(intervention group) were educated to a smoking prevention program for 1 year from April 1999 to April 2000. School-based four-class program to prevent smoking was developed. The program provides instruction about short and long-term negative physiologic and social consequences of smoking and also discussed the health hazards of smoking, social pressure to smoke, peer norms regarding tobacco use, and refusal skill. A 45-item self-administered structured questionnaire was designed to evaluate the change of knowledge, attitude, smoking rate and the amount of smoking. The instrument was comprised of 11 knowledge items, thirteen attitude item and demographic items. Each scales were created by summing responses to each items within each scales and high scores on the knowledge, attitude, and smoking behavioral intention scales indicated positive responses. Based on the changes before and after the implementation of smoking prevention program between intervention and control group, the change of scores on knowledge were significantly different between the control group and the intervention group(p<0.05) and the change of scores on the attitude toward smoking was significantly different between intervention and control group. The change of smoking rate were not showing a significant difference between two groups but the amount of smoking were significantly reduced in intervention group than control group. In multiple regression analysis on changes of knowledge about smoking, the variables of smoking prevention program education, previous knowledge on smoking and students' school performance were selected the significant variables. In multiple regression to analysis of the factors influencing changes in attitude toward smoking, the variables of smoking prevention program education, previous knowledge on smoking were shown to be significant. The smoking prevention program was effective on change of knowledge and attitude of middle school students. In considering that the policy should be needed to extent of implementation of school-based health education curricula based on social influence model and it would contribute to reduce smoking of students.

• Tuberculosis and Respiratory Diseases
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• v.45 no.2
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• pp.290-300
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• 1998

Background: CYFRA 21-1 is a tumor marker which measures a fragment of cytokeratin 19 expressed by epithelial cells in bronchus. It is known that cytokeratin 19 is abundant in squamous epithelial cell cancer of the lung. However, if the incidence of elevated serum CYFRA 21-1 level in patients with benign lung diseases or pulmonary tuberculosis with severe parenchymal damage is high the specificity of CYFRA 21-1 could be decreased. The purpose of this study is to investigate the changes of serum CYFRA 21-1 according to the degree of parenchymal damage and the usefulness of CYFRA 21-1 for diagnosing possibly combined lung cancer in patients with pulmonary tuberculosis. Method: We studied the changes of serum CYFRA 21-1 according to the sputum AFB stain, radiologic manifestation and history of treatment in 81 patients with pulmonary tuberculosis, and 20 healthy persons, 25 patients with lung cancer, as a control group. CYFRA 21-1 concentration in serum was quantified by the immunoradiometry assay(Centocor$^{(R)}$). Result: The results were as follow; Serum CYFRA 21-1 level was significantly lower in patients with pulmonary tuberculosis($1.54{\pm}1.19ng/mL$, p<0.01) as compared to patients with lung cancer($12.25{\pm}15.97ng/mL$), and was slightly higher than the level in heathy persons($0.90{\pm}0.49ng/mL$) but there was no significant difference. Serum CYFRA 21-1 level was below the cut-off value of 3.3ng/mL in 95 percent of patients with pulmonary tuberculosis but it was above the cut-off value in 64 percent of patients with lung cancer. Serum CYFRA 21-1 level was significantly higher in the initial treatment group($1.91{\pm}1.55ng/mL$, p<0.05) as compared to the treatment. failure group ($0.92{\pm}0.30ng/mL$). According to the sputum AFB smear, serum CYFRA 21-1 level in patients with negative result was slightly higher than the level in patients with positive result but there was no significant difference. According to the radiologic manifestation, serum CYFRA 21-1 level was significantly higher in patients with infiltrative lesion ($2.15{\pm}1.63ng/mL$, p<0.01) as compared to patients with destructive lesion ($l.04{\pm}0.54ng/mL$). As the size of cavity or destructive lesion was larger, the level was significantly lower(p<0.05). Conclusion: As serum CYFRA 21-1 level was significantly higher in the initial treatment group and patients with infiltrative lesion, it suppose to be closely related with the degree of parenchymal damage of the lung of the pulmonary tuberculosis. However CYFRA 21-1 could be useful method for diagnosing lung cancer even in patients with pulmonary tuberculosis combined with lung cancer because of the fact that it was below the cutoff value of 3.3ng/mL in 95 percent of patients with pulmonary tuberculosis.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 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
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• v.19 no.1
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• pp.35-55
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• 2013

Information technology is a critical resource necessary for any company hoping to support and realize its strategic goals, which contribute to growth promotion and sustainable development. The selection of information technology and its strategic use are imperative for the enhanced performance of every aspect of company management, leading a wide range of companies to have invested continuously in information technology. Despite researchers, managers, and policy makers' keen interest in how information technology contributes to organizational performance, there is uncertainty and debate about the result of information technology investment. In other words, researchers and managers cannot easily identify the independent factors that can impact the investment performance of information technology. This is mainly owing to the fact that many factors, ranging from the internal components of a company, strategies, and external customers, are interconnected with the investment performance of information technology. Using an agent-based simulation technique, this research extracts factors expected to affect investment performance on information technology, simplifies the analyses of their relationship with economic modeling, and examines the performance dependent on changes in the factors. In terms of economic modeling, I expand the model that highlights the way in which product quality moderates the relationship between information technology investments and economic performance (Thatcher and Pingry, 2004) by considering the cost of information technology investment and the demand creation resulting from product quality enhancement. For quality enhancement and its consequences for demand creation, I apply the concept of information quality and decision-maker quality (Raghunathan, 1999). This concept implies that the investment on information technology improves the quality of information, which, in turn, improves decision quality and performance, thus enhancing the level of product or service quality. Additionally, I consider the effect of word of mouth among consumers, which creates new demand for a product or service through the information diffusion effect. This demand creation is analyzed with an agent-based simulation model that is widely used for network analyses. Results show that the investment on information technology enhances the quality of a company's product or service, which indirectly affects the economic performance of that company, particularly with regard to factors such as consumer surplus, company profit, and company productivity. Specifically, when a company makes its initial investment in information technology, the resultant increase in the quality of a company's product or service immediately has a positive effect on consumer surplus, but the investment cost has a negative effect on company productivity and profit. As time goes by, the enhancement of the quality of that company's product or service creates new consumer demand through the information diffusion effect. Finally, the new demand positively affects the company's profit and productivity. In terms of the investment strategy for information technology, this study's results also reveal that the selection of information technology needs to be based on analysis of service and the network effect of customers, and demonstrate that information technology implementation should fit into the company's business strategy. Specifically, if a company seeks the short-term enhancement of company performance, it needs to have a one-shot strategy (making a large investment at one time). On the other hand, if a company seeks a long-term sustainable profit structure, it needs to have a split strategy (making several small investments at different times). The findings from this study make several contributions to the literature. In terms of methodology, the study integrates both economic modeling and simulation technique in order to overcome the limitations of each methodology. It also indicates the mediating effect of product quality on the relationship between information technology and the performance of a company. Finally, it analyzes the effect of information technology investment strategies and information diffusion among consumers on the investment performance of information technology.

• Korean Journal of Breeding Science
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• v.41 no.1
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• pp.16-24
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• 2009

Although leaf characters are important in soybean [Glycin max (L.) Merr.] breeding and development of cultural methods, very little information has been reported. The objectives of this study were to evaluate and analyze the relationships among leaf characters and suggest possible classification criteria for cultivating and wild (Glycin soja Sieb. & Zucc.) soybeans. Total of 94 cultivating and 91 wild soybean accessions from the Soybean Germplasm Laboratory of Chungbuk National University were used for this study. Central leaflet of the second leaf from the top of the plant was selected to measure leaf characters. Average leaf length, leaf width, leaf area, leaf shape index (LSI) of cultivating and wild soybeans were 12.3$\pm$1.25 cm and 6.6$\pm$1.35 cm, 6.8$\pm$1.241 cm and 2.9$\pm$0.92 cm, 55.6$\pm$15.75 $cm^2$ and 14.3$\pm$7.83 $cm^2$, and 1.9$\pm$0.38 and 2.4$\pm$0.53, respectively. Based on LSI, three categories of leaf shape, i.e., oval, ovate and lanceolate, were defined as LIS$\leq$2.0, LSI 2.1~3.0 and 3.1$\leq$LSI, respectively. Percentage of oval, ovate and lanceolate leaf types among cultivating and wild soybean accessions were 78.7%, 17.0% and 4.3 %, and 40%, 15.4% and 4.4%, respectively. Based on leaf length, three categories for cultivating, i.e. short leaf ($\leq$11.0 cm), intermediate (11.1~13.0 cm), and long (13.1 cm$\leq$), and four categories, i.e. short ($\leq$5.0 cm), intermediate (5.1~7.0 cm), long (7.0~9.0 cm), and very long (9.1 cm$\leq$) for wild soybeans were defined. Short, intermediate and long leaf types were about 1/3, 1/2 and 1/6, respectively, in cultivating soybeans, and 15.4%, 40.7% and 39.5%, plus 4.4% of very long leaf type in wild soybean. Cultivating and wild soybeans had leaf thickness, leaf area ratio (LAR), angle and petiol length of 0.25$\pm$0.054 mm and 0.14$\pm$0.032 mm, 40.1$\pm$8.22 and 53.7$\pm$12.02, $37.6{\pm}5.89^{\circ}$ and $54.6{\pm}10.77^{\circ}$, and 23.9$\pm$5.89 cm and 5.9$\pm$2.33 cm, respectively. There were highly significant positive correlations between leaf length and leaf width, and negative correlation between LSI and leaf width both in cultivating and wild soybeans. Although leaf area showed significant correlations with leaf length, leaf width and LIS in cultivating soybeans, wild soybeans showed no significant relationships among these characters. In general, soybeans with oval, ovate and lanceolate leaves were significantly different in leaf width and thickness. Cultivating soybean with oval leaf had greater leaf area, while wild soybeans with oval or ovate leaf had longer petiol than with lanceolate leaf.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.4
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• pp.39-49
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• 2018

"Annals of The Joseon Dynasty" is a book recording the Joseon Dynasty's historical facts in an annalistic format. The King's amusement activities through "Annals of The Joseon Dynasty" which were established by the Ye-ak(禮樂) system were analyzed. The results are as follows. The king's amusement activities that were performed during the Joseon Dynasty period could be classified as state banquets, military banquets, and banquets for play. The analysis of the king's amusement activity was divided into five stages. The characteristic of [1 period : King Taejo~Sejo(Yejong)] was dominated the military banquets of the Goryeo Dynasty. Neo-Confucianism is the establishment of political and social turning of the ballast, considerations of military culture, culture, and Hoeryeyeon Jinpungjeong, a cloud of dust and elders banquets such as Giroyeon and Yangnoyeon on the nature of the party. A lasting ordinance was institutionalized[2 period : King Seongjong~Jungjong]. In the chopper and jeongyujaeran, Hong Kyung Rae led a royal amusement activities are stagnant, often produce isolated storage compute in the gloomy situation[3 period : King Injong~Hyeonjong]. Revival period is pride of the amusement activity through the culture of Joseon Dynasty royal culture [4 period : King Sukjong~Jeongjo]. The throne, crashed due to political power is an ebb of royal amusement activities, while also rapidly waning[5 period : King Seonjo~Seonjong]. During the early Joseon Dynasty, hunting took place around the forest area northeast of Hanyang and during King Seongjong's period, it took place closer to the capital city, while in Lord Yeonsan's period, it was expanded to a 39 kilometer radius area from the palace, and banquets such as various forms of entertainment of Cheoyongmu, and Flower-viewing. The Joseon kings who enjoyed hunting were King Sejong, Sejo, Seongjong, Yeonsan, and Jungjong. Most of hunting objects were tigers, bears, deer and roe deer, leopards, boars, their animals and falconry took, and the purpose of the hunting was to perform ancestral rites to the royal ancestry or the royal tombs. Lord Yeonsan's hunting activities had negative effects after King Jungjong the king's hunting activity decreased sharply. However, there were also positive aspects of Lord Yeonsan's Prohibition of cutting woods ect. In conclusion, the expansion of the King's garden(庭:courtyard${\rightarrow}$園:privacy garden${\rightarrow}$苑:king's garden${\rightarrow}$苑?:national hunting park) is evident which starts from formal and informal activities that took place in Oejo, Chijo, and Yeonjo, which went further to the separate and secret gardens, and then even further, thus setting the amusement activity area as a 39 kilometer radius range from Hanyang.