• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Chest Surgery
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• v.41 no.6
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• pp.679-686
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• 2008

Background: Cellular remnants in the bioprosthetic heart valve are known to be related to a host's immunologic response and they can form the nidus for calcification. The extracellular matrix of the decellularized valve tissue can also be used as a biological scaffold for cell attachment, endothelialization and tissue reconstitution. Thus, decellularization is the most important part in making a bioprosthetic valve and biological caffold. Many protocols and agents have been suggested for decellularization, yet there ave been few reports about the effect of a treatment with hypotonic solution prior to chemical or enzymatic treatment. This study investigated the effect of a treatment with hypotonic solution and the appropriate environments such as temperature, the treatment duration and the concentration of sodium dodecylsulfate (SDS) for achieving proper decellularization. Material and Method: Porcine aortic valves were decellularized with odium dodecylsulfate at various concentrations (0.25%, 0.5%), time durations (6, 12, 24 hours) and temperatures ($4^{\circ}C$, $20^{\circ}C$)(Group B). Same the number of porcine aortic valves (group A) was treated with hypotonic solution prior to SDS treatment at the same conditions. The duration of exposure to the hypotonic solution was 4, 7 and 14 hours and he temperature was $4^{\circ}C$ and $20^{\circ}C$, respectively. The degree of decellularization was analyzed by performing hematoxylin and eosin staining. Result: There were no differences in the degree of decellularization between the two concentrations (0.25% 0.5%) of SDS. Twenty four hours treatment with SDS revealed the best decellularization effect for both roups A and B at the temperature of $4^{\circ}C$, but there was no differences between the roups at $20^{\circ}C$. Treatment with hypotonic solution (group A) showed a better ecellularization effect at all the matched conditions. Fourteen hours treatment at $4^{\circ}C$ ith ypotonic solution prior to 80S treatment revealed the best decellularization effect. The treatment with hypotonic solution at $20^{\circ}C$ revealed a good decellularization effect, but his showed significant extracellular matrix destruction. Conclusion: The exposure of porcine heart valves to hypotonic solution prior to SDS treatment is highly effective for achieving decellularization. Osmotic treatment with hypotonic solution should be considered or achieving decellularization of porcine aortic valves. Further study should be carried out to see whether the treatment with hypotonic solution could reduce the exposure duration and concentration of chemical detergents, and also to evaluate how the structure of the extracellular matrix of the porcine valve is affected by the exposure to hypotonic solution.

• Animal Systematics, Evolution and Diversity
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• v.11 no.1
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• pp.39-64
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• 1995

A faunistic and ecological study on the occurrence of freshwater cladocerans and copepods was accomplished from Chindo, South Korea. Collections were made from total 35 stations, comprising the various freshwater habitats like reservoirs, streams, swamps, bogs, ricefields, ditch, pond, and spring during the periods of July 23-25, and November 1-3 in 1994. Twenty seven cladoceran species of 17 genera of 6 families in 2 orders, and 28 copepod species of 21 genera of 6 families in 3 orders were collected during this research period, of which Daphnia obtusa Kurz and Elaphoidella bidens (Schmeil) are newly recorded from Korea. In reservoirs, Diaphanosoma sp. and Thermocyclops taihokuensis were dominant in July, and then succeeded by Bosmina longirostris and Cyclops vicinus vicinus in November. Thermocyclops crassus co-occurred with 7: taihokuensis at both seasons, was frequent in November after T. taihokuensis precipitately decreased. In other stagnant waters, 7: taihokuensis and Moina weismanni were dominant at ponds in July and in November, respectively. At ricefields in July Moina macrocopa and T. taihokuensis were dominant, but in November M. macrocopa and Paracyclops fimbriatus were. At streams, cladocerans were relatively rare, but became more rich in November. The representative cladoceran species were Bosmina longirostris as a plankton, and Chydorus sphaericus as a epibenthic species. Concerning copepods, nearly all the stations of streams except a few ones adjacent to seashore showed the similiar species constitutions, of which E. serrulatus and M, pehpeiensis were most frequent and abundant. At a mountain streamlet and a spring, the occurrence of Alona sp., Attheyella byblis Chang and Kim, 1992 and A. tetraspinosa Chang, 1993 is quite interesting and deserved much attention in the taxonomical point of view. Seventeen major cladocerans and copepods from lentic habitats and 13 major cladocerans and copepods from lotic habitatats were clustered using average taxonomic distance and UPGMA to infer the co-occurrence relations among species. As for lentic habitats, two large phena were appeared at first. The one phenon consisted of Diaphanosoma sp. and T taihokuensis, and showed its predominancy over the various habitats and its dominancy was rapidly decreased in November. The other phenon frequently occurred rather in November, and subdivided into three subgroups. On the other hand, as for lotic habitats, 13 species were also grouped into 2 large phena. The first one comprised 4 species, which were dominant and highly frequent at nearly all the lotic habitats, and subdivided into three subgroups according to their seasonal fluctuation types. The second one was also subdivided into three phena, the first of which comprised only one species, Microcyclops varicans, and occurred at most of the stations along stream with steadiness through the research period; the second phenon, Chydorus sphaericus, occurred much frequently in November; the last phenon included a few heterogenous subgroups.

• Tuberculosis and Respiratory Diseases
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• v.54 no.2
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• pp.178-190
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• 2003

Background : The committee of tuberculosis(TB) survey planning for the year 2000 decided to construct the Korean Tuberculosis Surveillance System (KTBS), based on a doctor's routine reporting method. The successful keys of the KTBS rely on the precision of the recorded TB notification forms. The purpose of this study was to determine that the accuracy of the TB notification form written at a private general hospital given to the corresponding health center and to improve the comprehensiveness of these reporting systems. Materials and Methods : 291 adult TB patients who had been diagnosed from August 2000 to January 2001, were enrolled in this study. The lists of TB notification forms were compared with the medical records and the various laboratory results; case characteristics, history of previous treatment, examinations for diagnosis, site of the TB by the international classification of the disease, and treatment. Results : In the list of examinations for a diagnosis in 222 pulmonary TB patients, the concordance rate of the 'sputum smear exam' was 76% but that of the 'sputum culture exam' was only 23%. Among the 198 cases of the sputum culture exam labeled 'not examined', 43(21.7%) cases proved to be true 'not examined', 70 cases(35.4%) were proven to be 'culture positive', and 85(43.0%) cases were proven to be 'culture negative'. In the list of examinations for a diagnosis in 69 extrapulmonary TB patients, the concordance rate of the 'smear exam other than sputum' was 54%. In the list of treatments, the overall concordance rate of the 'type of registration' in the TB notification form was 85%. Among the 246 'new' cases on the TB notification form, 217(88%) cases were true 'new' cases and 13 were proven to be 'relapse', 2 were proven to be 'treatment after failure', one was proven to be 'treatment after default', 12 were proven to be 'transferred-in' and one was proven to be 'chronic'. Among the 204 HREZ prescribed regimen, 172(84.3%) patients were taking the HREZ regimen, and the others were prescribed other drug regimens. Conclusion : Correct recording of the TB notification form at the private sectors is necessary for supporting the effective TB surveillance system in Korea.