• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of Life Science
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• v.22 no.12
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• pp.1628-1636
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• 2012

This study examined the antioxidant activity of the dark purple rice seeds from the rice line, MGI079, derived from insertional mutagenesis. The contents of polyphenolic compounds were 1.3 and 1.9-fold higher in the MGI079-2-1 and MGI079-2-6 rice lines than in the donor cultivar MGI079. Flavonoid contents were 6.4-fold higher in the MGI079-2-1 line. The MGI079-2-1 line showed a 24.4-fold higher activity in DPPH free radical scavenging compared to the MGI079 line. The anthocyanin content of the MGI079-2-6 line was more than 106.4-fold higher than the MGI079 line and 1.4-fold higher than the Heugnam line. Anthocyanin content in colored rice grains was negatively correlated with Hunter's L, a, and b values, with the correlation coefficients of $-5.64^{**}$, $5.21^{**}$ and -1.15, respectively. The grain length/width of a mutant of MGI079 segregated to a medium and bold type compared to the medium type of MGI079. However, the 1,000 grain weight was decreased to 13.6~19.6 g compared to 19.8 g for MGI079. Amylose content of the endosperm was 5.6~23.8% higher than in the MGI079 line. The grain of mutants of MGI079 was distinguished by its starch characteristics. The higher antioxidant activity of the MGI079-2-1 and MGI079-2-6 lines indicated functional characteristics associated with high-value resources, so future breeding should focus on the development of pigments in colored rice in new varieties.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

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

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

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.197-218
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• 2019

In addition to economic growth and national income increase, China is also experiencing rapid growth in consumption of cosmetics. About 67% of the total trade volume of Chinese cosmetics is made by e-commerce and especially K-Beauty products, which are Korean cosmetics are very popular. According to previous studies, 80% of consumer goods such as cosmetics are affected by the word of mouth information, searching the product information before purchase. Mostly, consumers acquire information related to cosmetics through comments made by other consumers on SNS such as SINA Weibo and Wechat, and recently they also use information about beauty related video channels. Most of the previous online word-of-mouth researches were mainly focused on media itself such as Facebook, Twitter, and blogs. However, the informational characteristics and the expression forms are also diverse. Typical types are text, picture, and video. This study focused on these types. We analyze the unstructured data of SINA Weibo, the SNS representative platform of China, and Meipai, the video platform, and analyze the impact of K-Beauty brand sales by dividing online word-of-mouth information with quantity and direction information. We analyzed about 330,000 data from Meipai, and 110,000 data from SINA Weibo and analyzed the basic properties of cosmetics. As a result of analysis, the amount of online word-of-mouth information has a positive effect on the sales of cosmetics irrespective of the type of media. However, the online videos showed higher impacts than the pictures and texts. Therefore, it is more effective for companies to carry out advertising and promotional activities in parallel with the existing SNS as well as video related information. It is understood that it is important to generate the frequency of exposure irrespective of media type. The positiveness of the video media was significant but the positiveness of the picture and text media was not significant. Due to the nature of information types, the amount of information in video media is more than that in text-oriented media, and video-related channels are emerging all over the world. In particular, China has made a number of video platforms in recent years and has enjoyed popularity among teenagers and thirties. As a result, existing SNS users are being dispersed to video media. We also analyzed the effect of online type of information on the online cosmetics sales by dividing the product type of cosmetics into basic cosmetics and color cosmetics. As a result, basic cosmetics had a positive effect on the sales according to the number of online videos and it was affected by the negative information of the videos. In the case of basic cosmetics, effects or characteristics do not appear immediately like color cosmetics, so information such as changes after use is often transmitted over a period of time. Therefore, it is important for companies to move more quickly to issues generated from video media. Color cosmetics are largely influenced by negative oral statements and sensitive to picture and text-oriented media. Information such as picture and text has the advantage and disadvantage that the process of making it can be made easier than video. Therefore, complaints and opinions are generally expressed in SNS quickly and immediately. Finally, we analyzed how product diversity affects sales according to online word of mouth information type. As a result of the analysis, it can be confirmed that when a variety of products are introduced in a video channel, they have a positive effect on online cosmetics sales. The significance of this study in the theoretical aspect is that, as in the previous studies, online sales have basically proved that K-Beauty cosmetics are also influenced by word-of-mouth. However this study focused on media types and both media have a positive impact on sales, as in previous studies, but it has been proven that video is more informative and influencing than text, depending on media abundance. In addition, according to the existing research on information direction, it is said that the negative influence has more influence, but in the basic study, the correlation is not significant, but the effect of negation in the case of color cosmetics is large. In the case of temporal fashion products such as color cosmetics, fast oral effect is influenced. In practical terms, it is expected that it will be helpful to use advertising strategies on the sales and advertising strategy of K-Beauty cosmetics in China by distinguishing basic and color cosmetics. In addition, it can be said that it recognized the importance of a video advertising strategy such as YouTube and one-person media. The results of this study can be used as basic data for analyzing the big data in understanding the Chinese cosmetics market and establishing appropriate strategies and marketing utilization of related companies.

• Tuberculosis and Respiratory Diseases
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• v.59 no.5
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• pp.522-529
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• 2005

Background : Several national societies have published guidelines for empirical antimicrobial therapy in patients with severe community-acquired pneumonia (SCAP). This study investigated the etiologies of SCAP in the Asan Medical Center and assessed the relationship between the initial empirical antimicrobial regimen and 30 day mortality rate. Method : retrospective analysis was performed on patients with SCAP admitted to the ICU between March 2002 and February 2004 in the Asan Medical Center. The basic demographic data, bacteriologic study results and initial antimicrobial regimen were examined for all patients. The clinical outcomes including the ICU length of stay, the ICU mortality rate, and 30 days mortality rates were assessed by the initial antimicrobial regimen. Results : One hundred sixteen consecutive patients were admitted to the ICU (mean age 66.5 years, 81.9 % male, 30 days mortality 28.4 %). The microbiologic diagnosis was established in 58 patients (50 %). The most common pathogens were S. pneumoniae (n=12), P. aeruginosae (n=9), K. pneumonia (n=9) and S. aureus (n=8). The initial empirical antimicrobial regimens were classified as: ${\beta}$-lactam plus macrolide; ${\beta}$-lactam plus fluoroquinolone; anti-Pseudomonal ${\beta}$-lactam plus fluoroquinolone; Aminoglycoside combination regimen; ${\beta}$-lactam plus clindamycin; and ${\beta}$-lactam alone. There were no statistical significant differences in the 30-day mortality rate according to the initial antimicrobial regimen (p = 0.682). Multivariate analysis revealed that acute renal failure, acute respiratory distress syndrome and K. pneumonae were independent risk factors related to the 30 day mortality rate. Conclusion : S. pneumoniae, P. aeruginosae, K. pneumonia and S. aureus were the most common causative pathogens in patients with SCAP and K. pneumoniae was an independent risk factor for 30 day mortality. The initial antimicrobial regimen was not associated with the 30-day mortality.

• Journal of Chest Surgery
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• v.43 no.6
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• pp.681-686
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• 2010

Background: A chest computed-tomography has become more prevalent so that it is more common to detect small sized pulmonary nodules that have not been found in previous simple chest x-ray. If those detected nodules are undersized or located in pulmonary parenchyma, it is difficult to accomplish a biopsy since it is vulnerable to explore them either grossly or digitally. Thus, in our hospital, a thoracoscopic pulmonary wedge resection was performed after locating a lesion by means of hook wire with CT-guided. Material and Method: 31 patients (17 males and 14 female patients) from December in 2006 to June in 2010 became our subjects; their 34 pulmonary nodules were subjected to the thoracoscopic pulmonary wedge resection after locating a lesion by means of hook wire with CT-guided. Also we analyzed a possibility of hook wire dislocation, a frequency of conversion to open thoracotomy, time consumed to operation after location of a lesion, operation time, post operation complication, and histological diagnosis of the lesion. Result: 12 of 34 cases were ground glass lesion, whereas 22 cases of them were solitary pulmonary lesion. The median value of the lesion was 8mm in size (range: 3 to 23 mm), while the median value was 12.5 mm in depth (range: 1 to 34 mm). The median value of time consumed from location of the lesion to anesthetic induction was 86.5 minutes (41~473 minutes); furthermore the mean value of operation time was 103 minutes (25~345 minutes). Intrathoracic wire dislocation was found in one case, but a target lesion was successfully excised. Open thoracotomy was performed in four cases due to pleural adhesion. However, there was no case of conversion to open thoracotomy due to failure to detect a target lesion. In histological diagnosis, metastatic cancer were found in 15 cases, which were the most common, primary lung cancer were in 9 cases, non-specific inflammation were in 3 cases, tuberculosis inflammation were in 2 cases, lymph nodes were in 2 cases, active tuberculosis were in 1 case, atypical adenomatous hyperplasia was in 1 case and normal lung parenchymal finding was in 1 case, respectively. Conclusion: In our hospital, in order to accomplish a precise histological diagnosis of ground-glass lesion and pulmonary nodules in lung parenchyma, location of pulmonary nodules were exactly located with hook wire under chest computed-tomography, which was followed by lung biopsy. We concluded that this was an accurate, minimally invasive and valuable method to minimize the complications and increase of cost of medical service provided.

• Biomedical Science Letters
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• v.4 no.2
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• pp.143-151
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• 1998

To study the incidence and epidemiological factors of uterine cervical cancer in medical underserved area females, the questionnaire survey and Pap. smear for uterine cervical cancer was done on total 330 women who lived in Ullungdo from 5th to 12th August, 1998. The results were summarized as follows： The age distribution of subjects was 50s (24.5%), 60s (24.5%) and their educational level was “no schooling” (14.2%) and “elementary school” (42.7%). The first coital age of subjects was 19∼21yrs (30.0%), 16∼18yrs (13.9%) and the first pregnancy age was 22∼24yrs (36.7%) and 19∼21yrs (30.0%). The frequency of total pregnancy of subjects was over 5 times (52.1%). The frequency of total delivery was “3∼4 times” (35.5％) and “5∼6 times” (15.2％). 68.8% of subjects had experience of abortion and 80.0% of their husband were on the phimosis. 172 (52.1%) subjects had gynecological symptoms, their symptoms were leukorrhea (48.3%), pruritus (21.5%) and leukorrhea with pruritus (20.3%). 63.9% of total subjects have been received Pap. smear and the frequency of their Pap. smear was “only 1 time” (44.1 ％), “irregularly” (30.3%) and the reason of respondents who have not been received Pap. smea. was “no specific symptom” (51.3%). Among the 330 women screened there were negative (45.8%), inflammation (47.3%), trichomoniasis and candidiasis (1.8%), atypical cells (4.5%) and dysplasia (0.6%).

• Korean Journal of Clinical Laboratory Science
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• v.47 no.3
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• pp.132-139
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• 2015

Recently, $p16^{INK4a}$/Ki-67 dual immunostaining has been introduced as a new biomarker protocol for early detection of uterine cervical dysplasia and cancer in liquid-based cytology (LBC). We performed the $p16^{INK4a}$/Ki-67 dual immunostaining using a CINtec$^{(R)}$ PLUS kit in a total of 109 LBC cases of cervicovaginal smear and compared its results with those from LBC, HPV hybrid capture II (HC II) test and histological diagnosis. Expression of $p16^{INK4a}$ and Ki-67 was significantly associated with cases of LSIL or higher in cytological diagnosis and cases of cervical intraepithelial neoplasia (CIN) 1 or higher in histological diagnosis (p<0.001 and p<0.001, respectively). Among forty-six cases of atypical squamous cells of undetermined significance (ASCUS) in LBC, $p16^{INK4a}$ and Ki-67 was expressed in 31 (67.4%), which were positively associated with cases of CIN I lesion or higher in histology. The sensitivity of $p16^{INK4a}$/Ki-67 dual immunostaining for finding lesions of CIN 1 or higher was 89.0%, which was higher than LBC. The specificity was 73.5%, which was higher than that of the HC II test. Based on these results, the $p16^{INK4a}$/Ki-67 dual immunostaining method can be a useful diagnostic marker for improving the sensitivity of LBC and the specificity of HC II test.