• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Proceedings of the Korean Journal of Food and Nutrition Conference
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• 2001.12a
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• pp.39-74
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• 2001

The endeavors enhancing the grain quality of high-yielding japonica rice were steadily continued during 1980s∼1990s along with the self-sufficiency of rice production and the increasing demands of high-quality rices. During this time, considerably great, progress and success was obtained in development of high-quality japonica cultivars and qualify evaluation techniques including the elucidation of interrelationship between the physicochemical properties of rice grain and the physical or palatability components of cooked rice. In 1990s, some high-quality japonica rice caltivars and special rices adaptable for food processing such as large kernel, chalky endosperm aromatic and colored rices were developed and its objective preference and utility was also examined by a palatability meter, rapid-visco analyzer and texture analyzer. The water uptake rate and the maximum water absorption ratio showed significantly negative correlations with the K/Mg ratio and alkali digestion value(ADV) of milled rice. The rice materials showing the higher amount of hot water absorption exhibited the larger volume expansion of cooked rice. The harder rices with lower moisture content revealed the higher rate of water uptake at twenty minutes after soaking and the higher ratio of maximum water uptake under the room temperature condition. These water uptake characteristics were not associated with the protein and amylose contents of milled rice and the palatability of cooked rice. The water/rice ratio (in w/w basis) for optimum cooking was averaged to 1.52 in dry milled rices (12% wet basis) with varietal range from 1.45 to 1.61 and the expansion ratio of milled rice after proper boiling was average to 2.63(in v/v basis). The major physicochemical components of rice grain associated with the palatability of cooked rice were examined using japonica rice materials showing narrow varietal variation in grain size and shape, alkali digestibility, gel consistency, amylose and protein contents, but considerable difference in appearance and torture of cooked rice. The glossiness or gross palatability score of cooked rice were closely associated with the peak. hot paste and consistency viscosities of viscogram with year difference. The high-quality rice variety “Ilpumbyeo” showed less portion of amylose on the outer layer of milled rice grain and less and slower change in iodine blue value of extracted paste during twenty minutes of boiling. This highly palatable rice also exhibited very fine net structure in outer layer and fine-spongy and well-swollen shape of gelatinized starch granules in inner layer and core of cooked rice kernel compared with the poor palatable rice through image of scanning electronic mcroscope. Gross sensory score of cooked rice could be estimated by multiple linear regression formula, deduced from relationship between rice quality components mentioned above and eating quality of cooked rice, with high Probability of determination. The ${\alpha}$ -amylose-iodine method was adopted for checking the varietal difference in retrogradation of cooked rice. The rice cultivars revealing the relatively slow retrogradation in aged cooked rice were Ilpumbyeo, Chucheongbyeo, Sasanishiki, Jinbubyeo and Koshihikari. A Tongil-type rice, Taebaegbyeo, and a japonica cultivar, Seomjinbyeo, shelved the relatively fast deterioration of cooked rice. Generally, the better rice cultivars in eating quality of cooked rice showed less retrogiadation and much sponginess in cooled cooked rice. Also, the rice varieties exhibiting less retrogradation in cooled cooked rice revealed higher hot viscosity and lower cool viscosity of rice flour in amylogram. The sponginess of cooled cooked rice was closely associated with magnesium content and volume expansion of cooked rice. The hardness-changed ratio of cooked rice by cooling was negatively correlated with solids amount extracted during boiling and volume expansion of cooked rice. The major physicochemical properties of rice grain closely related to the palatability of cooked rice may be directly or indirectly associated with the retrogradation characteristics of cooked rice. The softer gel consistency and lower amylose content in milled rice revealed the higher ratio of popped rice and larger bulk density of popping. The stronger hardness of rice grain showed relatively higher ratio of popping and the more chalky or less translucent rice exhibited the lower ratio of intact popped brown rice. The potassium and magnesium contents of milled rice were negatively associated with gross score of noodle making mixed with wheat flour in half and the better rice for noodle making revealed relatively less amount of solid extraction during boiling. The more volume expansion of batters for making brown rice bread resulted the better loaf formation and more springiness in rice bread. The higher protein rices produced relatively the more moist white rice bread. The springiness of rice bread was also significantly correlated with high amylose content and hard gel consistency. The completely chalky and large gram rices showed better suitability for fermentation and brewing. Our breeding efforts on rice quality improvement for the future should focus on enhancement of palatability of cooked rice and marketing qualify as well as the diversification in morphological and physicochemical characteristics of rice grain for various value-added rice food processings.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.29 no.3
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• pp.197-205
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• 2007

Angiogenesis is a essential part for bone formation and bone fracture healing. Vascular endothelial growth factor (VEGF), one of the most important molecules among many angiogenic factors, is a specific mitogen for vascular endothelial cells. VEGF-mediated angiogenesis is required for bone formation and repair. However, the effect of VEGF on osteoblastic cells during osteogenesis is still controversial. In recent days, substantial progress have been made toward developing tissue-engineered alternatives to autologous bone grafting for maxillofacial bony defects. Periosteum has received considerable interest as a better source of adult stem cells. Periosteum has the advantage of easy harvest and contains various cell types and progenitor cells that are able to differentiate into a several mesenchymal lineages, including bone. Several studies have reported the bone formation potential of periosteal cells, however, the correlation between VEGF signaling and cultured human periosteal cell-derived osteogenesis has not been fully investigated yet. The purpose of this study was to examine the correlation between VEGF signaling and cultured human periosteal-derived cells osteogenesis. Periosteal tissues of $5\;{\times}\;20\;mm$ were obtained from mandible during surgical extraction of lower impacted third molar from 3 patients. Periosteal-derived cells were introduced into the cell culture and were subcultured once they reached confluence. After passage 3, the periosteal-derived cells were further cultured for 42 days in an osteogenic inductive culture medium containing dexamethasone, ascorbic acid, and ${\beta}-glycerophosphate$. We evaluated the alkaline phosphatase (ALP) activity, the expression of Runx2 and VEGF, alizarin red S staining, and the quantification of osteocalcin and VEGF secretion in the periosteal-derived cells. The ALP activity increased rapidly up to day 14, followed by decrease in activity to day 35. Runx2 was expressed strongly at day 7, followed by decreased expression at day 14, and its expression was not observed thereafter. Both VEGF 165 and VEGF 121 were expressed strongly at day 35 and 42 of culture, particularly during the later stages of differentiation. Alizarin red S-positive nodules were first observed on day 14 and then increased in number during the entire culture period. Osteocalcin and VEGF were first detected in the culture medium on day 14, and their levels increased thereafter in a time-dependent manner. These results suggest that VEGF secretion from cultured human periosteal-derived cells increases along with mineralization process of the extracellular matrix. The level of VEGF secretion from periosteal-derived cells might depend on the extent of osteoblastic differentiation.

• Journal of the Mineralogical Society of Korea
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• v.23 no.2
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• pp.125-139
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• 2010

Behavior of heavy metals and arsenic in the tailings of Songcheon Au-Ag mine was characterized via both mineralogical and geochemical methods. Mineral composition of the tailings was investigated by X-ray diffractometry, energy-dispersive spectroscopy, and electron probe micro-analyzer (EPMA) and total concentrations of heavy metals and arsenic and their chemical forms were analyzed by total digestion of aqua regia and sequential extraction method, respectively. The results of mineralogical study indicate that the tailings included mineral particles of resinous shape mainly consisting of galena, sphalerite, pyrite, quartz, and scorodite, and specifically socordite was identified in the form of matrix. EPMA quantitative analyses were performed to evaluate the weatherability of each mineral, and the results suggest that it decreased in the sequence of arsenopyrite > galena > sphalerite > pyrite. The weathering pattern of galena was observed to show distinctive zonal structure consisting of secondary minerals such as anglesite and beudantite. In addition, almost all of arsenopyrite has been altered to scorodite existing asmatrix and galena, sphalerite, and pyrite which have lower weatherability than arsenopyrite were identified within the matrix of scorodite. During the process of alteration of arsenopyrite into scorodite, it is likely that a portion of arsenic was lixiviated and caused a great deal of detrimental effects to surrounding environment. The results of EPMA quantitative analyses verify that the stability of scorodite was relatively high and this stable scorodite has restrained the weathering of other primary minerals within tailings as a result of its coating of mineral surfaces. For this reason, Songcheon tailings show the characteristics of the first weathering stage, although they have been exposed to the surface environment for a long time. Based on the overall results of mineralogical and geochemical studies undertaken in this research, if the tailings are kept to be exposed to the surface environment and the weathering process is continuous, not only hazardous heavy metals, such as lead and arsenic seem to be significantly leached out because their larger portions are being partitioned in weakly-bound (highly-mobile) fractions, but the potential of arsenic leaching is likely to be high as the stability of scorodite is gradually decreased. Consequently, it is speculated that the environmental hazard of Songcheon mine is significantly high.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.155-174
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• 2022

From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (