• Clinical and Experimental Pediatrics
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• v.50 no.1
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• pp.33-39
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• 2007

Purpose : In spite of medical advances, empyema is a serious complication of pneumonia in children. Vaccination practices and antibiotic prescribing practices promote the change of clinical manifestations of empyema and causative organisms. So we made a nationwide clinical observation of 122 cases of empyema in children from 32 hospitals during the 5 year period from September 1999 to August 2004. Methods : Demographic data, and clinical information on the course and management of empyema patients were collected retrospectively from medical records in secondary and tertiary hospitals in Korea. Results : One hundred twenty two patients were enrolled from 35 hospitals. The most frequent age group was 1-3 years, accounting for 48 percent of all cases. The male to female sex ratio was 1.2:1. The main symptoms were cough, fever, respiratory difficulty, lethargy and chest pain in order of frequency. Hematologic findings on admission revealed decreased hemoglobin levels ($10.4{\pm}1.6g/dL$) and increased leukocyte counts ($16,234.3{\pm}10,601.8/{\mu}L$). Pleural fluid obtained from patients showed high leukocyte counts ($30,365.8{\pm}64,073.0/{\mu}L$), high protein levels ($522.3{\pm}1582.3g/dL$), and low glucose levels ($88.1{\pm}523.5mg/dL$). Findings from pleural fluid cultures were positive in 80 cases(65.6 percent). The most common causative agent was Streptococcus pneumoniae. The majority of patients were treated with antibiotics and closed drainage. Some patients needed open drainage (16.4 percent) or decortication (3.3 percent). The mean duration of hospitalization was $28.6{\pm}15.3days$. Conclusion : We analyzed childhood empyema patients during a period of 5 years in Korean children. The most frequent age group was 1-3 years and the most common causative agent was Streptococcus pneumoniaeiae. The majority of patients were treated with antibiotics and close drainage.

• Journal of the Korean Geographical Society
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• v.30 no.1
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• pp.68-92
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• 1995

Recentry the structure of the retail trade have been chanaed with its environmantal changes. Some studies may be necessary on the changing process of environment and fundamental structure analyses of the retail trade. This study analyzes the location of retail trades, inhabitants' behavior in retail tredes and their desirable utilization scheme of them in Kwangju-si. Some study methods, contents and coming-out results are as follows: 1. Retail trades can be classified into independent stores, chain-stores (supermarket, voluntary chain and frenchiise system and convenience store), department stores, cooperative associations, traditional, markets mail-order marketing, automatic vending and others by service levels, selling-items, prices, managements, methods of retailing and store or nonstore type. 2. In Kwangju, the environment of retail trades is related to the consumers of population structure: chanes in consumers pattern, trends toward agings and nuclear family, increase of leisur: time and female advances to society. Rapid structural shift in retail trade has also been occurred due to these social changes. Traditionl and premodern markets until 1970s altere to supermarkets or department stores in 1980s, and various types, large enterprises and foreign capitals came into being in 1990s. 3. The locational characteristics of retail trades are resulted from the spatial analysis of the total population distribution, and from the calculation of segregation index in the light of potential demand. The densely-populated areas occurs in newly-built apartment housing complex which is distributed with a ring-shaped pattern around the old urban core. The numbers and rates of the aged over sixty in Kwangsan-gu and the circumference area of Mt.Moodeung, are larger and higher where rural elements are remarkable. A relation between population distribution and retail trade are analysed by the index of population per shop. The index of the population number per shop is lower in urban center, as a whole, being more convenient for consumers. In newly-formed apartment complex areas, on the other, the index more than 1,000 per shop, meeting not the demands for consumers. Because both the younger and the aged are numerous in these areas, the retail trade pattern pertinent to both are needed. Urban fringes including Kwangsan-gu and the vicinity of Mt.Moodeung have some problems owing to the most of population number per shop (more than 1, 500) and the most extensive as well. 4. The regional characteristic of retail trade is analyzed through the location quotient of shops by locational patterns and centerality index. Chungkum-dong is the highest-order central place in CBD. It is the core of retail trades, which has higher-ordered specialty store including three big department stores, supermarkets and large stores. Taegum-dong, Chungsu-dong, Taeui-dong, and Numun-dong that are neiahbored to Chungkum-dong fall on the second group. They have a central commercial section where large chain stores, specialty shopping streets, narrow-line retailing shops (furniture, amusement service, and gallary), supermarkets and daily markets are located. The third group is formed on the axis of state roads linking to Naju-kun, Changseong-kun, Tamyang-kun, Hwasun-kun and forme-Songjeong-eup. It is related to newly, rising apartment housing complex along a trunk road, and characterized by markets and specialty stores. The fourth group has neibourhood-shopping centers including older residential area and Songjeong-eup area with independent stores and supermarkets as main retailing functions. The last group contains inner residential area and outer part of a city including Songjeong-eup. Outer part of miscellaneous shops being occasionally found is rural rather than urban (Fig. 7). 5. The residents' behaviors using retail trade are analyzed by factors of goods and facilities. Department stores are very high level in preference for higher-order shopping-goods such as clothes for full dress in view of both diversity and quality of goods(28.9%). But they have severe traffic congestions, and high competitions for market ranges caused by their sma . 64.0% of respondents make combined purpose trips together with banking and shopping. 6. For more efficiency of retail-trading, it is necessary to induce spatial distribution policy with regard to opportunity frequency of goods selection by central place, frontier regions and age groups. Also we must consider to analyze competition among different types of retail trade and analyze the consumption behaviors of working females and younger-aged groups, in aspects of time and space. Service improvement and the rationalization of management should be accomplished in such as cooperative location (situation) must be under consideration in relations to other functions such as finance, leisure & sports, and culture centers. Various service systems such as installment, credit card and peremium ticket, new used by enterprises, must also be carried service improvement. The rationalization and professionalization in for the commercial goods are bsically requested.

• Journal of Nutrition and Health
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• v.44 no.6
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• pp.537-550
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• 2011

This study was conducted to identify behavioral characteristics of salty food intake according to saline sensitivity of adults living in a rural area. Anthropometry and blood pressure were measured and salt intake-related dietary behavior was surveyed by questionnaires through interviews with 402 subjects aged ${\geq}$ 40 years in Chungcheongbuk-Do, Korea. The percentages of overweight and obese among the subjects were 37.8% and 3.8% respectively. Mean blood pressure of the subjects was in the normal range, but the distribution of subjects who were normotensive, high normal, and hypertensive was 48.7%, 17.7%, and 33.6% respectively. Approximately 27% of all subjects habitually consumed salty food, which was the smallest group, followed by 38.1% normal and 35.1% not-salty food. However, 34.6% of the eldest group of ${\geq}$ 65 years consumed salty food. The saline insensitive group showed a higher percentage of irregular meals, overeating, speed-eating, an unbalanced diet, a preference for fried food, and habitual intake of salty foods. These subjects recognized the risk for eating salty food, but they lacked the will to reduce their salty food intake. Compared to spouses and family members, experts such as doctors, nurses, and dieticians were the most influential for reducing the salty food intake of subjects. Saline sensitive group had relatively better control over salty food intake at every meal, eating out, and even when eating salty food that the spouse preferred. The saline sensitive group ate more frequently vegetables and fruits, whereas the saline insensitive group ate more frequently hot spicy foods. In conclusion, the results suggest that it is necessary to establish a social atmosphere toward reducing salt intake at the level of the government and food industry and to set action plans to be available for nutrition education programs to reduce salt intake nationwide.

• Journal of Korean Society of Forest Science
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• v.56 no.1
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• pp.66-76
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• 1982

Soil harness represents such physical properties as porosity, amount of water, bulk density and soil texture. It is very important to know the mechanical properties of soil as well as the chemical in order to research the fundamental phenomena in the growth and the distribution of tree roots. The writer intended to grip soil hardness by soil layer and also to grasp the root distribution and the correlation between soil hardness and the root distribution of Pinus riguda Mill. planted on the denuded hillside with sooding works by soil layer on soil profile. The site investigated is situated at Peongchang-ri 13, Kocksung county, Chon-nam Province. The area is consisted of 3.63 ha having on elevation of 167.5-207.5 m. Soil texture is sandy loam and parant rock in granite. Average slope of the area is $17^{\circ}-30^{\circ}$. Soil moisture condition is dry. Main exposure of the area is NW or SW. The total number of plots investigated was 24 plots. It divided into two groups by direction each 12 plots in NW and SW and divided into three groups by the position of mountain plots in foot of mountain, in hillside, and in summit of mountain, respectively. Each sampling tree was selected as specimen by purposive sampling and soil profile was made at the downward distance of 50cm form the sampling tree at each plot. Soil hardness, soil layer surveying, root distribution of the tree and vegetation were measured and investigated at the each plot. The soil hardness measured by the Yamanaka Soil Hardness Tester in mm unit. the results are as follows: 1) Soil hardness increases gradually in conformity with the increment of soil depth. The average soil indicator hardness by soil layer are as follows: 14.6mm in I - soil layer (0-10cm in depth from soil surface), 16.2mm in II - soil layer (10-20cm), 17.2 in III - soil layer (20-30cm), 18.3mm in IV - soil layer(30-40cm), 19.8mm in V - soil layer (4.50mm). 2) The tree roots (less than 20mm in diameter) distribute more in the surface layer than in the subsoil layer and decrease gradually according to the increment of soil depth. The ratio of the root distribution can be illustrated by comparing with each of five soil layers from surface to subsoil layer as follows: I - soil layer; 31%, II - soil layer; 26%, III - soil layer; 18%, IV - soil layer; 12%, V - soil layer; 13%, 3) Soil hardness and tree root distribution (less than 20mm in diameter) of Pinus rigida Mill. correlate negatively each other; the more soil hardness increases, the most root distribution decreases. The correlation coefficients between soil hardness and distribution of tree roots by soil layer are as follows: I - soil layer; -0.3675 (at the 10% significance level), II - soil layer; -0.5299 (at the 1% significance level), III - soil layer; -0.5573 (at the 2% significance level), IV - soil layer; -0.6922 (at the 5% significance level), V - soil layer; -0.7325 (at the 2% significance level). 4) the most suitable range of soil hardness for the growth of Pinus rigida Mill is the range of 12-14.9mm in soil indicator hardness. In this range of soil indicator hardness, the root distribution of this tree amounts to 41.8% in spite of 33% in soil harness and under the 20.9mm of soil indicator hardness, the distribution amounts to 93.2% in spite of 82% in soil hardness. Judging from above facts, the roots of Pinus rigida can easily grow within the soil condition of 20.9mm in soil indicator hardness. 5) The soil layers are classified by their depths from the surface soil.

• Journal of Korean Home Economics Education Association
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• v.30 no.2
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• pp.39-56
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• 2018

The purpose of this study was to develop Home Economics(HE) teaching and learning process plans applied Problem Based Learning(PBL) focusing on 'food and nutrition' unit for students with intellectual disability and to evaluate the effects of the HE instruction on their food choice·management knowledge and problem-solving skills after implementing the instruction for students with intellectual disability. To develop HE teaching and learning process plans applied PBL focusing on 'food and nutrition' unit for students with intellectual disability, problems that arise in daily life to trigger interest of students were firstly developed. The selected problems and teaching and learning process plans were reviewed for validity by one home economics education professor and three teachers who are experts in special education. This study used the one group pretest and posttest design, sampling 6 students who are in special-education middle school with the intellectual disability. After HE instruction of 6 sessions applied PBL method, this study tested the effects of the instruction. The first three sessions taught how to choose and keep food. The fourth session taught purchasing food ingredients and keeping them for sandwiches. The fifth and sixth sessions let the students make sandwiches and give them to others. The instruments of the study comprised of tools for food choice and management knowledge, tools for problem-solving skills evaluation, self-evaluation sheets, evaluation form of course satisfaction for students, evaluation form of behavior in class for teachers, and daily observation journal and all tools. These instruments were proved to have reliability and validity. The results of this study are as follows. First, all six students who took HE instruction applied PBL method focusing on 'food and nutrition' unit scored 30 points higher out of 100 points after taking the instruction in food choice and management knowledge and scored 5 points higher out of 14 points in problem-solving skills on average. Therefore, it was interpreted that HE instruction applied PBL affected the food choice·management knowledge and the problem solving skills of students with intellectual disability. Secondly, the students with intellectual disability participated actively in HE instruction applied PBL focusing on 'food and nutrition' unit and expressed satisfaction. Three special education experts evaluated HE teaching·learning process plans applied PBL focusing on 'food and nutrition' unit to be well-developed. This study showed that HE instruction applied PBL focusing on 'food and nutrition' unit allowed the students with intellectual disability to acquire comprehensive skills in choosing, keeping, and making safe food and helped them solve problems of their life by themselves. Therefore I suggest that Home Economics should be adopted as a formal subject matter in special school curriculum for students with intellectual disability.

• Journal of agricultural medicine and community health
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• v.20 no.2
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• pp.175-185
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• 1995

This study is to compare family support and mental health between the rural and the urban elderly. In order to do that I collected the data through questioning 238 people in 3 urban areas in Busan and 201 people in 9 rural areas near Daegu. The degree of their family support is 36.70 on the average in the rural area and 40.77 in the urban area. The degree of family support of urban elderly is a little higher. According to general characters between the differences of family support in both areas, in the rural area there are differences in sex, age, whether they have a spouse or not, education level, financial state, number of children, number of co living, status of co living, subjective health status, amount of pocket money and how much they are participating in leisure activity. In the urban area there are differences in sex, whether they have a spouse or not, religion, financial state, number of co living, status of co living, subjective health status, amount of pocket money, how much they are participating in leisure activity and house pattern. In the stepwise multiple regression analysis the main variables that affect degree of family support in the rural area are age, whether they have a spouse or not and financial state which account for 33% of the total variance and in the urban area are subjective health status, financial state, whether they have a spouse or not and number of co-living which account for 35%. Health status is better in the urban area(average 36.87) than in the rural area(57.42). In each item the people whose mark was more than 75%(low) have Depression 8.4%, Somatization 8.0% in the urban area and Somatization 8.5%, Depression 8.5%, Anxiety 4.0%, Phobic anxiety 4.0%, Obsessive compulsive reaction 2.5%, Hostility 2.0%, Paranoid ideation 2.0%, Psychoticism 1.5% and Interpersonal sensitivity 1.5% in the rural area. In the mental health condition, on the basis of 4 points in both areas, the average is Somatization(rural : 1.69, urban : 1.51), Depression (rural : 1.64, urban : 1.37) and Obsessive compulsive reaction(rural : 1.33, urban : 0.99). According to the differences between mental health conditions by general characters, in the rural area the differences are presented in sex, age, whether they have a spouse or not, religion, education level, financial state, number of children, status of co living, subjective health status, amount of pocket money and how much they are participating in leisure activity, in the urban area the differences are presented in sex, whether they have a spouse or not, religion, financial state, number of co living, status of co living, subjective health status, house pattern, amount of pocket money and how much they are participating in leisure activity. In the stepwise multiple regression analysis the main variables that affect mental health condition in the rural are family support degree subjective health status, religion sex, age and financial state which account for 43% of the total and in the urban area are family support degree, subjective health status and financial state which account for 51%. In the matter of family support degree and mental health condition the rural area was -0.4555, of urban area was -0.6446. The rural area that has a high percentage in family support degree and mental health condition Depression was -0.5036, Psychoticism was -0.4265 in the urban area Psychoticism was -0.6452, Depression was -0.5955. Family support has a great influence on mental health of old people and family support and mental health condition can be different according to living area. So in their problems nursing intervention through family and nursing strategies according to living area should be established.

• Korean Security Journal
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• no.48
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• pp.9-33
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• 2016

The purpose of the present study was to find out cause-and-effect relationship between job requirements and job resources, with job burnout as a mediator variable, and the effects of these variables on organizational effectiveness. The population in the present study was private security guards employed by 13 private security companies in Seoul and Gyeonggi-do areas, and a survey was conducted on 500 security guards selected using purposive sampling technique. Out of 460 questionnaires distributed, 429 responses, excluding 31 outliers or insincere responses, were used for data analysis. For analysis, data were coded and entered into SPSS 18.0 and AMOS 18.0, which were used to analyze the data. Descriptive analyses were performed to find out sociodemographic characteristics of the respondents. The exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to test the validity of the measurement tool, and the Cronbach's Alpha coefficients were calculated to test the reliability. To find out the significance of relationships among variables, Pearson's correlation analysis was performed. Covariance Structure Analysis (CSA) was performed to test the relationship among latent factors of a model for job requirements, job resources, job burnout, and organizational effectiveness of the private security guards, and the fitness of the model analyzed with CSA was determined by the goodness-of-fit index ($x^2$, df, p, RMR, GFI, CFI, TLI, RMSEA). The level of significance was set at .05, and the following results were obtained. First, even though the effect of job requirements on job burnout was not statistically significant, it had a positive influence overall, and this result can be considered such that the higher the perception of job requirements by the member of the organization, the higher the perception of job burnout. Second, the influence of job resources on job burnout was negative, which can be considered that the higher the perception of job resources, the lower the perception of job burnout. Third, even though the influence of job requirements on organizational effectiveness was statistically nonsignificant, it had a negative influence overall, and this result can be considered that the higher the perception of job requirements, the lower the perception of organizational effectiveness. Fourth, job resources had a positive influence on organizational effectiveness, and it can be considered that the higher the perception of job resources, the higher the perception of organizational effectiveness. Fifth, the results of the analysis between job burnout and organizational effectiveness revealed that, even though the influence of job burnout on organizational effectiveness was statistically nonsignificant, it had partial negative influences on sublevels of organizational effectiveness, and this may suggest that the higher the perception of job burnout by the organization members, the lower the organizational effectiveness. Sixth, the analysis of mediating role in the relationship between job requirements and organizational effectiveness, job burnout was taking partial mediating role between job requirements and organizational effectiveness. These results suggest that reducing job burnout by managing job requirements, organizational effectiveness that leads to job satisfaction, organizational commitment, and turnover intention can be maximized. Seventh, the analysis of mediating role in the relationship among job requirements, job resources, and organizational effectiveness, job burnout was assuming a partial mediating role in the relationships among job requirements, job resources, and organizational effectiveness. These results suggest that organizational effectiveness can be maximized by either lowering job requirements or burnout management through reorganizing job resources.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.