• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of the Daesoon Academy of Sciences
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• v.35
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• pp.217-253
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• 2020

This study analyzes the principles of the 'Earthly Paradise' (仙境, the realm of immortals), 'Virtuous Concordance of Yin and Yang' (陰陽合德), and the 'Reordering Works of Heaven and Earth' (天地公事) while combining them with Joseon art. Therefore, this study aims to discover the context wherein the concept of Taiji in 'Daesoon Truth,' deeply penetrates into Joseon art. Doing so reveals how 'Daesoon Thought' is embedded in the lives and customs of the Korean people. In addition, this study follows a review of the sentiments and intellectual traditions of the Korean people based on 'Daesoon Thought' and creative works. Moreover, 'Daesoon Thought' brings all of this to the forefront in academics and art at the cosmological level. The purpose of this research is to vividly reveal the core of 'Daesoon Thought' as a visual image. Through this, the combination of 'Daesoon Thought' and Joseon art will secure both data and reality at the same time. As part of this, this study deals with the world of 'Daesoon Thought' as a cosmological Taiji principle. This concept is revealed in Joseon art, which is analyzed and examined from the viewpoint of art philosophy. First, as a way to make use of 'Daesoon Thought,' 'Daesoon Truth' was developed and directly applied to Joseon art. In this way, reflections on Korean life within 'Daesoon Thought' can be revealed. In this regard, the selection of Joseon art used in this study highlights creative works that have been deeply ingrained into people's lives. For example, as 'Daesoon Thought' appears to focus on the genre painting, folk painting, and landscape painting of the Joseon Dynasty, attention is given to verifying these cases. This study analyzes 'Daesoon Thought,' which borrows from Joseon art, from the perspective of art philosophy. Accordingly, attempts are made to find examples of the 'Virtuous Concordance of Yin and Yang' and Tai-Ji in Joseon art which became a basis by which 'Daesoon Thought' was communicated to people. In addition, appreciating 'Daesoon Thought' in Joseon art is an opportunity to vividly examine not only the Joseon art style but also the life, consciousness, and mental world of the Korean people. As part of this, Chapter 2 made several findings related to the formation of 'Daesoon Thought.' In Chapter 3, the structures of the ideas of 'Earthly Paradise' and 'Virtuous Concordance of Yin and Yang' were likewise found to have support. And 'The Reordering Works of Heaven and Earth' and Tai-Ji were found in depictions of metaphysical laws. To this end, the laws of 'The Reordering Works of Heaven and Earth' and the structure of Tai-Ji were combined. In chapter 4, we analyzed the 'Daesoon Thought' in the life and work of the Korean people at the level of the convergence of 'Daeesoon Thought' and Joseon art. The analysis of works provides a glimpse into the precise identity of 'Daesoon Thought' as observable in Joseon art, as doing so is useful for generating empirical data. For example, works such as Tai-Jido, Ssanggeum Daemu, Jusachaebujeokdo, Hwajogi Myeonghwabundo, and Gyeongdodo are objects that inspired descriptions of 'Earthly Paradise', 'Virtuous Concordance of Yin and Yang,' and 'The Reordering Works of Heaven and Earth.' As a result, Tai-Ji which appears in 'Daesoon Thought', proved the status of people in Joseon art. Given all of these statements, the Tai-Ji idea pursued by Daesoon Thought is a providence that follows change as all things are mutually created. In other words, it was derived that Tai-Ji ideology sits profoundly in the lives of the Korean people and responds mutually to the providence that converges with 'Mutual Beneficence.'

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Tuberculosis and Respiratory Diseases
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• v.41 no.4
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• pp.339-353
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• 1994

Background : The p53 gene codes for a DNA-binding nuclear phosphoprotein that appears to inhibit the progression of cells from the G1 to the S phase of the cell cycle. Mutations of the p53 gene are common in a wide variety of human cancers, including lung cancer. In lung cancers, point mutations of the p53 gene have been found in all histological types including approximately 45% of resected NSCLC and even more frequently in SCLC specimens. Mutant forms of the p53 protein have transforming activity and interfere with the cell-cycle regulatory function of the wild-type protein. The majority of p53 gene mutations produce proteins with altered conformation and prolonged half life; these mutant proteins accumulate in the cell nucleus and can be detected by immunohistochemical staining. But protein overexpression has been reported in the absence of mutation. p53 protein overexpression or gene mutation is reported poor prognostic factor in breast cancer, but in lung cancer, its prognostic significance is controversial. Method : We investigated the p53 abnormalities by nucleotide sequencing, polymerase chain reaction-single strand conformation polymorphism(PCR-SSCP), and immunohistochemical staining. We correlated these results with each other and survival in 75 patients with NSCLC resected with curative intent. Overexpression of the p53 protein was studied immunohistochemically in archival paraffin- embedded tumor samples using the D07(Novocastra, U.K.) antibody. Overexpression of p53 protein was defined by the nuclear staining of greater than 25% immunopositive cells in tumors. Detection of p53 gene mutation was done by PCR-SSCP and nucleotide sequencing from the exon 5-9 of p53 gene. Result: 1) Of the 75 patients, 36%(27/75) showed p53 overexpression by immunohistochemical stain. There was no survival difference between positive and negative p53 immunostaining(overall median survival of 26 months, disease free median survival of 13 months in both groups). 2) By PCR-SSCP, 27.6%(16/58) of the patients showed mobility shift. There was no significant difference in survival according to mobility shift(overall median survival of 27 in patients without mobility shift vs 20 months in patients with mobility shift, disease free median survival of 8 months vs 10 months respectively). 3) Nucleotide sequence was analysed from 29 patients, and 34.5%(10/29) had mutant p53 sequence. Patients with the presence of gene mutations showed tendency to shortened survival compared with the patients with no mutation(overall median survival of 22 vs 27 months, disease free median survival of 10 vs 20 months), but there was no statistical significance. 4) The sensitivity and specificity of immunostain based on PCR-SSCP was 67.0%, 74.0%, and that of the PCR-SSCP based on the nucleotide sequencing was 91.8%, 96.2% respectively. The concordance rate between the immunostain and PCR-SSCP was 62.5%, and the rate between the PCR-SSCP and nucleotide sequencing was 95.3%. Conclusion : In terms of detection of p53 gene mutation, PCR-SSCP was superior to immunostaining. p53 gene abnormalities either overexpression or mutation were not a significant prognostic factor in NSCLC patients resected with curative intent. However, patients with the mutated p53 gene showed the trends of early relapse.