• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

• 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.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.625-634
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• 2003

More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.08a
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• pp.154-155
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• 2012

The promise of nano-crystalites (nc) as a technological material, for applications including display backplane, and solar cells, may ultimately depend on tailoring their behavior through doping and crystallinity. Impurities can strongly modify electronic and optical properties of bulk and nc semiconductors. Highly doped dopant also effect structural properties (both grain size, crystal fraction) of nc-Si thin film. As discussed in several literatures, P atoms or radicals have the tendency to reside on the surface of nc. The P-radical segregation on the nano-grain surfaces that called self-purification may reduce the possibility of new nucleation because of the five-coordination of P. In addition, the P doping levels of ${\sim}2{\times}10^{21}\;at/cm^3$ is the solubility limitation of P in Si; the solubility of nc thin film should be smaller. Therefore, the non-activated P tends to segregate on the grain boundaries and the surface of nc. These mechanisms could prevent new nucleation on the existing grain surface. Therefore, most researches shown that highly doped nc-thin film by using conventional PECVD deposition system tended to have low crystallinity, where the formation energy of nucleation should be higher than the nc surface in the intrinsic materials. If the deposition technology that can make highly doped and simultaneously highly crystallized nc at low temperature, it can lead processes of next generation flexible devices. Recently, we are developing a novel CVD technology with a neutral particle beam (NPB) source, named as neutral beam assisted CVD (NBaCVD), which controls the energy of incident neutral particles in the range of 1~300eV in order to enhance the atomic activation and crystalline of thin films at low temperatures. During the formation of the nc-/pm-Si thin films by the NBaCVD with various process conditions, NPB energy directly controlled by the reflector bias and effectively increased crystal fraction (~80%) by uniformly distributed nc grains with 3~10 nm size. In the case of phosphorous doped Si thin films, the doping efficiency also increased as increasing the reflector bias (i.e. increasing NPB energy). At 330V of reflector bias, activation energy of the doped nc-Si thin film reduced as low as 0.001 eV. This means dopants are fully occupied as substitutional site, even though the Si thin film has nano-sized grain structure. And activated dopant concentration is recorded as high as up to 1020 #/$cm^3$ at very low process temperature (＜ $80^{\circ}C$) process without any post annealing. Theoretical solubility for the higher dopant concentration in Si thin film for order of 1020 #/$cm^3$ can be done only high temperature process or post annealing over $650^{\circ}C$. In general, as decreasing the grain size, the dopant binding energy increases as ratio of 1 of diameter of grain and the dopant hardly be activated. The highly doped nc-Si thin film by low-temperature NBaCVD process had smaller average grain size under 10 nm (measured by GIWAXS, GISAXS and TEM analysis), but achieved very higher activation of phosphorous dopant; NB energy sufficiently transports its energy to doping and crystallization even though without supplying additional thermal energy. TEM image shows that incubation layer does not formed between nc-Si film and SiO2 under later and highly crystallized nc-Si film is constructed with uniformly distributed nano-grains in polymorphous tissues. The nucleation should be start at the first layer on the SiO2 later, but it hardly growth to be cone-shaped micro-size grains. The nc-grain evenly embedded pm-Si thin film can be formatted by competition of the nucleation and the crystal growing, which depend on the NPB energies. In the evaluation of the light soaking degradation of photoconductivity, while conventional intrinsic and n-type doped a-Si thin films appeared typical degradation of photoconductivity, all of the nc-Si thin films processed by the NBaCVD show only a few % of degradation of it. From FTIR and RAMAN spectra, the energetic hydrogen NB atoms passivate nano-grain boundaries during the NBaCVD process because of the high diffusivity and chemical potential of hydrogen atoms.

• Journal of Korean Society of Transportation
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• v.18 no.1
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• pp.75-85
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• 2000

In existing models in optimization, the crisp data improve has been used in the objective or constraints to derive the optimal solution, Besides, the subjective environments are eliminated because the complex and uncertain circumstances were regarded as Probable ambiguity, In other words those optimal solutions in the existing models could be the complete satisfactory solutions to the objective functions in the Process of application for industrial engineering methods to minimize risks of decision-making. As a result of those, decision-makers in location Problems couldn't face appropriately with the variation of demand as well as other variables and couldn't Provide the chance of wide selection because of the insufficient information. So under the circumstance. it has been to develop the model for the location and size decision problems of logistics facility in the use of the fuzzy theory in the intention of making the most reasonable decision in the Point of subjective view under ambiguous circumstances, in the foundation of the existing decision-making problems which must satisfy the constraints to optimize the objective function in strictly given conditions in this study. Introducing the Process used in this study after the establishment of a general mixed integer Programming(MIP) model based upon the result of existing studies to decide the location and size simultaneously, a fuzzy mixed integer Programming(FMIP) model has been developed in the use of fuzzy theory. And the general linear Programming software, LINDO 6.01 has been used to simulate, to evaluate the developed model with the examples and to judge of the appropriateness and adaptability of the model(FMIP) in the real world.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.2
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• pp.129-139
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• 2013

This paper is targeted on proposing ameliorating alternative to performance assessment method of GSEM through evaluating the current one, which is initiated by SMBA to induce fair competition among 5 GSEM across the country and accommodate the quality improvement of entrepreneurship education since 2005 after beginning the SMBA support, from the perspective of BSC(Balanced Scorecard) tool. Ultimately, it complements the policy defects of SMBA over GSEM, in particular, in the process of performance assessment and management. This paper carries out two studies as follow. First, throughout reviewing the previous studies relating to BSC applications to non-profit organization, it set out the direction of introducing BSC in assessing performance of GSEM in order to enhance its effectiveness. Second, it evaluate the rationality of performance assessing tools apllied to GSEM by SMBA on the basis of BSC application over non-profit organization, especially in education institution. Research results shows the following implications. First, the current evaluation system over GSEM is just merely assessment itself and not much contributions for the post performance management. Second, The annual evaluation just remains to check up whether the policy goals are met or not. Third, the current evaluation puts much emphasis just on financial inputs and hardware infra, not considering human resources and utilization of government policy and institution. Fourth, the policy goals are unilaterally focused on entrepreneurs. Fifth, the current evaluation systems do not contain any indexes relating to learning and growth perspectives for concerning sustainable and independent growing up. However, lack of empirical testing require this paper to need the further study in the future.

• Journal of Science of Art and Design
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• v.11
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• pp.5-35
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• 2007

When it comes to art, the two conflicting themes of 'scientific progress' and the 'nature' have often motivated the advent of the new mode of arts. By the late 20th century, uniform and simplified mode of arts, inspired by scientific and technological progress of that time, was gradually disillusioned by the contemporaries due to the adverse effect of science on human life. In this context, naturalism pursuing for harmony of human and the nature came up as an alternative to those living in the 21st century. The pendulum has swung from minimalism to naturalism. Though the quantitative improvement of human life cannot be denied, the uprise of such problems as environmental pollution and exhaustion of natural resources degraded the quality of human life, which, eventually, shifted the attention to the theme - 'revival of naturality.' Therefore, this thesis intends to represent the modem interpretation of the 'revival of naturality' by applying the major expressions of Art Nouveau that also emphasized naturalism. Art Nouveau found its motifs from organic figures of natural beings and put them to designs of decorative arts. This carries a historical significance in that Art Nouveau boldly revolutionized historicism, which only repeated adoption and modification of the existing modes of arts, and opened it to the modem design with new attempts to practical applications of the arts. Art Nouveau, which means 'new art', prospered from the late 19th century to the early 20th century, and even after one century, it is highly appreciated, reviving as novel and vivid forms in this contemporary art. Art Nouveau based on naturalism has revisited our contemporary period when naturalism and feminized romanticism came into fashion and its common motifs revive in different jenre of arts such as fashion, furniture, glass works, and jewelry works. This study illustrates and analyzes the works of four major artists who gave a specific attention to botanical motifs of Art Nouveau and applies decorative beauty of highly sophisticated and organic curved lines and the expressional forms of botanical figures to design. Art Nouveau proved this; the nature herself is as beautiful as she can be. Within Art Nouveau, the true humanism can be revitalized with the 'revival of naturality'. This study rediscovered the boundless potential of modern interpretation and application of Art Nouveau in decorative art and design.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Applied Biological Chemistry
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• v.16 no.3
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• pp.146-165
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• 1973

Crop damages caused by sulfur dioxide poisoning were studied with respect to physiology of lesion, yield loss and prevention measures. The results are summarized as follows; 1. On the physiology of injury: The sulfur dioxide gas did no: affect the pH and $E_h$ values of the tested leaf juice of plants. Peroxidase activity was inhibited just after sulfur dioxide treatment but gradually recovered to normal after 10 hours. Methanolic chlorophyll solution was instantaneously and irreversibly bleached by the addition of sulfur dioxide gas with no evidence of pheophytin formation. It seems that chlorophyll forms colourless addition product or is reduced to colourless form with either sulfur dioxide gas or sulfurous acid. Chlorophyll in the chloroplast was also bleached by the sulfur dioxide treatment, as in the case of methanolic solution of chlorophyll, except that the rate of bleaching was rather slow, requiring 1-2 hours. It appears that the most inflicting cause of sulfur dioxide gas to plants may be the destruction of chlorophyll by the poisoning gas. 2. On the effects to crop yield: The crop yield losses were proportional to the concentration of inflicting sulfur dioxide gas. The order of tolerence of the crops to the sulfur dioxide gas was as follows - chinese cabbage being the most susceptible; wheat, paddy rice, barley, soybean, welsh onion, radish and chinese cabbage. The crucifer crops were generally found more susceptible than other crops studied. With respect to the growing stages of crops exposed to sulfur dioxide gas, it was found that the flowering stage was the most susceptible fellowed by panicle forming, milky and tillering in the decreasing order of susceptibility. 3. On the preventive measures of yield losses: Soil applications of potassium, wollastonite, lime or spray of lime water were effective to prevent yield losses from sulfur dioxide fumigation of paddy rice, barley, and soybeans. The most responsive treatment was lime water spray for all crops tested. In case of sulfur dioxide fumigated paddy rice, the lime water spray also increased carbon assimilation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.131-138
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• 2018

In this study, we investigated the trend of technological development in major countries related to the propulsion equipment of railway vehicles. The propulsion system is the main equipment of electric vehicles. A lot of time and investment are required in order to ensure the development of technology. Therefore, developed countries have maximized their effort to develop technologies with safety, reliability, and convenience of maintenance. They have also done their utmost to prevent technology transfer to other countries after the development of new technologies. For example, Toshiba of Japan developed a new 3,300V/1,500 A class IGBT power device, but was reluctant to export it to foreign countries in order to protect this technology. In this study, we analyzed the patents applied for related to propulsion control systems and presented the direction of development during the technical development of these systems. The patent analysis of the core technologies was conducted using the Thomson Innovation DB. We examined the number of patents applied for by country, year and major applicant. As a result of the analysis, it was found that the proportion of patent applications per country was in the order of China, 48%, Europe 16.6%, and the United States 14.9%. The patent situation of the top 10 principal applicants revealed that (the top three were?) ABB 14%, GE 13%, and CRRC 12%. At the same time, we also conducted a qualitative analysis of the level of technical development by evaluating such factors as the influence index, quotation, market securing power and citation. Based on the result of the patent analysis, we presented the direction of technical development of the propulsion control equipment of railway vehicles. Based on the analysis results, it was found that domestic applicants considerably reduced their efforts to protect their patents from foreign companies. Nowadays, most of the electric motors used in Korea are induction motors. In advanced countries, permanent magnet electric motors are employed in new railway lines. Therefore, intensive investment is needed in new developments.