• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.10
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• pp.1406-1413
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• 2016

This study was carried out to evaluate the toxicity of ethanolic extracts of Morus alba L. branch (ME). In the reverse mutation test, Salmonella Typhimurium TA98, TA100, TA1535, TA1357, and Escherichia coli WP2uvrA were used to estimate the mutagenic potential of ME. Sprague-Dawley rats were orally administered ME at levels of 1,250, 2,500, and 5,000 mg/kg for the single-dose toxicity test and 500, 1,000, and 2,000 mg/kg/d for the repeated-dose toxicity test for 28 consecutive days. As expected, reverse mutation was not detected at any concentration of ME, regardless of application of the metabolic activation system with or without S9 mix. In the single-dose toxicity test, ME caused neither significant visible signs of toxicity nor mortality in rats, and $LD_{50}$ was estimated to be over 5,000 mg/kg. In the repeated-dose toxicity test, ME administration at 500, 1,000, and 2,000 mg/kg for 28 days to male or female rats did not result in mortality. Similarly, no toxicologically significant treatment-related changes in body weight, food intake, or organ weights were noted. Several hematological and biochemical parameters in both genders showed significant differences, but these were within normal ranges. These results support the safe use of ME.

• Korean journal of applied entomology
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• v.53 no.3
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• pp.199-207
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• 2014

It is true that the proper environmental risk assessments for many GM (Genetically Modified) insects almost have not been executed in Korea. Therefore, we tested the environmental risk assessment about GM silkworms if there is any difference between GM silkworms and non-GM silkworms by the following three measurements. First, we measured their mobility in the breeding environment conditions with food and without food. Secondly, we measured their viability at the artificial extreme environmental conditions (low and high temperature and humidity, absent/present of foods,) after escaping from their breeding environments. Thirdly, we observed the number of laying eggs and their hatchability between GM silkworms and non-GM silkworms with four different pair experiments. The mobility of GM silkworms and non-GM silkworms statistically did not differ, and the egg productivity and hatchability were not also different. The hatchability by couple of GM female silkworms and non-GM male silkworms was lower than by non-GM male and female couple between the GM silkworms and non-GM silkworms, and there was statistically different. Relatively, the viability of GM silkworms was lower than non-GM silkworms. We could not exactly test for viability of silkworms in low temperature conditions because of their hibernating. Although there was any difference in viability and hatchability between GM silkworms and non-GM silkworms, all ability of GM silkworms was lower than non-GM silkworms. Conclusively, the environmental risk of GM silkworm was relatively lower than non-GM silkworm in this study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.460-471
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• 2019

The aim of this study was to examine the scale and market share of Chinese's research facility & equipment in the domestic research equipment market of basic and analytical science field for analyzing the difference of the number and amount of construction by year of acquisition, national research facility equipment standard classification code, and type of institution based on the information of the research equipment invested by the Korean government for the past 14 years. In addition, we analyzed the correlation among the year of acquisition, equipment standard classification code, and type of institution variables. As of January 1 2019, from 2005 to 2018, 50 Chinese's research facilities & equipments (main equipment with a construction cost of 30 million won or more) built in the basic and analytical science fields were selected for this study and their number of construction, amount of construction, year of acquisition, type of institution, and standard classification code were analyzed. Differences of the number and amount of construction with-in and by year of acquisition, standard classification code, and type of institution were tested using a single sample Chi-square test, Mann-Whitney U test, and Kruskal-wallis test. The correlation among the three variables was analyzed by using the Chi-square test of cross-tabulation analysis. And there was a statistically significant correlation among the year of acquisition, standard classification code, and type of institution (p<.05). Compared to the 2000s, in the 2010s, high-priced Optical Electronics/Video Equipment was installed at private universities, private enterprises, and government-affiliated research institute. Therefore, the domestic construction status of Chinese's research facility & equipment in the basic science and analytical science field is less than that of the domestic ones, but the number and the amount of construction are increasing statistically. So it is necessary for the government to be able to recognize the possibility that the Chinese's research facility and equipment can encroach on the domestic research industry market and to prepare related provision.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.