• 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 Korean Home Economics Education Association
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• v.25 no.1
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• pp.95-118
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• 2013

The purpose of this study was to analyze the contents of housing related to characters, objectives, and contents of FCS curricular from the 1st to 2009 revised curriculum of secondary school. The data were downloaded from the NCIC homepage(http://www.ncic.re.kr/2012. 04. 08) from the 1st(1955. 08) to 2009 revised curriculum(2012. 03) of secondary school. After examining the characters and objectives of each curriculum, contents of housing was analyzed by units and context elements of middle and high school separately. The titles of the subject, the objectives, the instructions, the leaning spheres, weekly hours, grade and gender of candidates, the emphasis of the instruction, etc. have been changed through the curriculum revision. The 6th curriculum was the main period to open to both genders, the $7^{th}$ was the period to combine with technology, the 2007 version was to change the structure of contents of home economics, and the 2009 version switched technology home economics from mandatory to optional in high school. The character of the courses was presented at the 1st curriculum, but it was left out from the $2^{nd}$ to $5^{th}$ curriculum. From the $6^{th}$ curriculum, the characters were separately given to middle and high school. The character of housing area started to appear only in high school home economics from the $7^{th}$ curriculum. The course objectives were described in all curriculum of both middle and high school. This applies to housing area as well. The course objectives have been modified in order to reflect value changes due to social issues. During each curriculum, contents of housing continued to change in context, course load, and candidates. Reflection of housing trends and social needs were the main causes of the change. 2009 version emphasizes on eco-life and sense of community.

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

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Korean Journal of Heritage: History & Science
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• v.52 no.2
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• pp.220-239
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• 2019

According to the National Currency (國幣) article in GyeongGukDaeJeon (經國大典), the ChoSonT'ongPaeJiIn (朝鮮通幣之印) was a seal that was imprinted on both ends of a piece of hemp fabric (布). It was used for the circulation of hemp fabric as a fabric currency (布幣). The issued fabric currency was used as a currency for trade or as pecuniary means to have one's crime exempted or replace one's labor duty. The ChoSonT'ongPaeJiIn would be imprinted on a piece of hemp fabric (布) to collect one-twentieth of tax. The ChoSonT'ongPaeJiIn (朝鮮通幣之印) was one of the historical currencies and seal materials used during the early Chosun dynasty. Its imprint was a means of collecting taxes; hence, it was one of the taxation research materials. Despite its value, however, there has been no active research undertaken on it. Thus, the investigator conducted comprehensive research on it based on related content found in JeonRokTongGo (典錄通考), Dae'JeonHu-Sok'Rok (大典後續錄), JeongHeonSwaeRok (貞軒?錄) and other geography books (地理志) as well as the materials mentioned by researchers in previous studies. The investigator demonstrated that the ChoSonT'ongPaeJiIn was established based on the concept of circulating Choson fabric notes (朝鮮布貨) with a seal on ChongOseungp'o (正五升布) in entreaty documents submitted in 1401 and that the fabric currency (布幣) with the imprint of the ChoSonT'ongPaeJiIn was used as a currency for trade, pecuniary or taxation means of having one's crime exempted, or replacing one's labor, and as a tool of revenue from ships. The use of ChoSonT'ongPaeJiIn continued even after a ban on fabric currencies (布幣) in March 1516 due to a policy on the "use of Joehwa (paper notes)" in 1515. It was still used as an official seal on local official documents in 1598. During the reign of King Yeongjo (英祖), it was used to make a military service (軍布) hemp fabric. Some records of 1779 indicate that it was used as a means of taxation for international trade. It is estimated that approximately 330 ChoSonT'ongPaeJiIn were in circulation based on records in JeongHeonSwaeRok (貞軒?錄). Although there was the imprint of ChoSonT'ongPaeJiIn in An Inquiry on Choson Currency (朝鮮貨幣考) published in 1940, there had been no fabric currencies (布幣) with its imprint on them or genuine cases of the seal. It was recently found among the artifacts of Wongaksa Temple. The seal imprint was also found on historical manuscripts produced at the Jikjisa Temple in 1775. The investigator compared the seal imprints found on the historical manuscripts of the Jikjisa Temple, attached to TapJwaJongJeonGji (塔左從政志), and published in An Inquiry on Choson Currency with the ChoSonT'ongPaeJiIn housed at the Wongaksa Temple. It was found that these seal imprints were the same shape as the one at Wongaksa Temple. In addition, their overall form was the same as the one depicted in Daerokji (大麓誌) and LiJaeNanGo (?齋亂藁). These findings demonstrate that the ChoSonT'ongPaeJiIn at Wongaksa Temple was a seal made in the 15th century and is, therefore, an important artifact in the study of Choson's currency history, taxation, and seals. There is a need for future research examining its various aspects.

• Journal of Korean Home Economics Education Association
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• v.29 no.4
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• pp.161-193
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• 2017

The purpose of this study was to develop and evaluate pre-parenthood education program(PPEP) based on Home Economics(HE) subject for high school students. The development and evaluation of PPEP based on HE subject in this study followed ADDIE model except implementation through 4 processes such as analysis, design, development, and evaluation. First, program development directions were set in three aspects such as 'general development', 'contents', and 'teaching and learning methods'. Themes of the program are 11 in total such as '1. Parenting, what is being a parent', '2. Choosing your spouse, happy marital relationship, the best gift to your children', '3. Pregnancy and birth, a moving meeting with a new life', '4. Taking care of a new born infant for 24 hours', '5. Taking care of infants, relationship with my lovely baby, attachment', '6. Taking care of young children, my child from another planet', '7. Parents and children in healthy family', '8. Parent-child relationship, wise parents to make effective interaction with their children', '9. Parents safety manager at home,', '10. Practice to take care of infants', and '11. Practice of community nurturing support service development'. In particular, learning activities of the program have major characteristics such as 1) utilization of cases including practice problems related to parenting, 2) community exchange activities utilizing learned knowledge and techniques, 3) actual life project activities utilizing learning contents related with parenting, 4) activities inducing positive changes in current life of high school students, and 5) practice activities for the necessities of life such as food, clothing and shelter supporting development of children. Second, the program was developed according to the design. Teaching-learning plans and materials for 17 classes were developed according to 11 themes. The developed plans include class flow and teacher's reference. It starts with receiving a class-related message from a virtual child at the introduction stage and ended with replying to the message by summarizing contents of the class and making a promise as a parent-to-be. That is the basic frame of class flow. Learning materials included various plans and reports necessary for learning activities and they are prepared in details so that they can be play the role of textbooks in regular curriculum. Third, evaluation of developed program was executed by a 5 point Likert scale survey on 13 HE experts on two aspects of program development process and program development results. In the evaluation of development process, mean value was 4.61 and index of content validity was 97.4%. For development results, mean value was 4.37 and index of content validity was 86.9%. These values showed that validity in the development process and results in this study was highly secured and confirmed that PPEP based on HE was appropriate and valid to enhance parent qualifications of high school learners.