• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.12
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• pp.723-732
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• 2017

As the development of ICT technology, in order to solve the problem of scattered water information's availability, WINS(Water management Information Networking System) by the Ministry of Land, Infrastructure and Transport was established and has been operated since 2004. However, there has been a disadvantage of providing specialized and limited information to the water resources sector mainly and a lack of active sharing of information because of no compulsory provision of information sharing between participants. In order to solve these problems, this paper carried out system development study, to do this, the status of domestic water information was surveyed and domestic and overseas related systems were compared and analyzed. The latest ICT technology was used to realize the contents as screen, and the user interface definition was created to present a role model of integrated water management through maximizing visualization by combining GIS and realtime data and providing space-time integrated information. These prior studies reached to actual construction of the ICT-based integrated management system for water information by K-water. This system is in service to the public installed in the water information portal, "MyWater".

• Journal of Distribution Science
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• v.12 no.8
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• pp.17-29
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• 2014

Purpose - The use of corporate social activities to implement the concept of corporate social responsibility enhances brand equity and attitude, and strengthens economic competitiveness. In areas such as mobile communications, companies take the responsibility of protecting customers and enhance the quality of the mobile communication service, helping to make an effort to obey the regulations of the public trade order and fair trade agreement, enabling a healthy society through communication with elderly living alone or youths without parents, and enhancing marketing strategies. Research design, data, and methodology - To test the hypothesis, a survey was conducted. The surveyed population includes people who use the big three mobile communication services. The survey was conducted from October 4th to October 14th, 2013. A total of 500 survey questionnaires were circulated and 483 were collected; out of these, 32 were excluded due to missing or incomprehensible information. The data was analyzed with SPSS 18.0 via frequency analysis, trust analysis, search factor analysis, relationship analysis, confirmation factor analysis using AMOS 18.0, and structural equation model analysis. Results - Research on corporate social responsibility has been frequently conducted recently. Companies are perceived as social constituents satisfying the social desires of people in addition to customer needs. Further, companies are returning profits to society to satisfy community needs, because there is greater emphasis on the social responsibilities of companies. Companies' social responsibilities should include marketing strategies and the identification of customer needs. This study shows that social service activities influence brand value, which influences customer attitudes; therefore, social service activities indirectly influence customer attitudes. In order to increase customers' purchasing intention, it is essential to improve brand image via social services and provide a distinctive quality of service. Conclusions - This research has used the purposive selection method in the empirical analysis to identify the effect of social services on brand value and customer attitude. Therefore, this study revealed that businesses, whose ultimate objective is to improve customers' purchasing intention, should promote their brand equity through corporate social responsibility activities and offer a distinct service quality. Limitations in the progress of research were found and future indications to overcome these limitations are suggested as follows. First, survey responders had a limited understanding of social responsibilities; therefore, this concept needs to be explained to people first. Second, the research was done on people who live in Daejeon; thus, it is not representative of the entire country. The research has to be repeated with people in other cities. Third, there is a limitation in the study because the purposive selection method was used on Daejeon customers. In the future, a more precise selection of the population is needed. Fourth, Daejeon has unique geographical and size characteristics. Thus, customers in Seoul and other areas may display different characteristics and research on them may reveal different findings. Therefore, again, this study has to be repeated in other areas.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.425-434
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• 2020

Background: Viral infection outbreaks are emerging public health concerns. They often exhibit seasonal patterns that could be predicted by the application of big data and bioinformatic analyses. Purpose: The purpose of this study was to identify trends in diarrhea-causing viruses such as rotavirus (Gr.A), norovirus G-I, and norovirus G-II in Cheonan, Korea. The identified related factors of diarrhea-causing viruses may be used to predict their trend and prevent their infections. Method: A retrospective analysis of 4,009 fecal samples from June 2010 to December 2019 was carried out at Dankook University Hospital in Cheonan. Reverse transcription-PCR (RT-PCR) was employed to identify virus strains. Information about seasonal patterns of infection was extracted and compared with local weather data. Results: Out of the 4,009 fecal samples tested using multiplex RT-PCR (mRT-PCR), 985 were positive for infection with Gr.A, G-I, and G-II. Out of these 985 cases, 95.3% (n = 939) were under 10 years of age. Gr.A, G-I, and G-II showed high infection rates in patients under 10 years of age. Student's t-test showed a significant correlation between the detection rate of Gr.A and the relative humidity. The detection rate of G-II significantly correlated with wind-chill temperature. Conclusion: Climate factors differentially modulate rotavirus and norovirus infection patterns. These observations provide novel insights into the seasonal impact on the pathogenesis of Gr.A, G-I, and G-II.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.91-98
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• 2024

With the advancement of ICT (Information and Communication Technology), searching for judgments through the internet has become increasingly convenient. However, predicting sentencing based on judgments remains a challenging task for individuals. This is because sentencing involves a complex process of applying aggravating and mitigating factors within the framework of legal provisions, and it often depends on the subjective judgment of the judge. Therefore, this research aimed to develop a model for predicting sentencing using artificial intelligence by focusing on structuring the data from judgments, making it suitable for AI applications. Through theoretical and statistical analysis of previous studies, we identified variables with high explanatory power for predicting sentencing. Additionally, by analyzing 50 legal judgments related to serious crimes that are publicly available, we presented a framework for extracting essential information from judgments. This framework encompasses basic case information, sentencing details, reasons for sentencing, the reasons for the determination of the sentence, as well as information about offenders, victims, and accomplices evident within the specific content of the judgments. This research is expected to contribute to the development of artificial intelligence technologies in the field of law in the future.

• Journal of Korea Entertainment Industry Association
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• v.13 no.3
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• pp.193-202
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• 2019

This study focuses on what changes are required as to a curriculum of dance department in the university in the age of the 4th industrial revolution. By comparing and analyzing the curricula of dance department in the five universities in Seoul, five academic subjects as to curricula of dance department, which covers what to learn for dance education in the age of the 4th industrial revolution, are presented. First, dance integrative education, the integration of creativity and science education, can be referred to as a subject that stimulates ideas and creativity and raises artistic sensitivity based on STEAM. Second, the curriculum characterized by prediction of the future prospect through Big Data can be utilized well in dealing with dance performance, career path of dance-majoring people, and job creation by analyzing public opinion, evaluation, and feelings. Third, video education. Seeing the images as modern major media tends to occupy most of the expressive area of art, dance by dint of video enables existing dance work to be created as new form of art, expanding dance boundaries in academic and performing art viewpoint. Fourth, VR and AR are essential techniques in the era of smart media. Whether upcoming dance studies are in the form of performance or education or industry, for VR and AR to be digitally applied into every relevant field, keeping with the time, learning about VR and AR is indispensable. Last, the 4th industrial revolution and the curriculum of dance art are needed to foresee the changes in the 4th industrial revolution and to educate changes, development and seeking in dance curriculum.

• Korean Journal of Microbiology
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• v.41 no.1
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• pp.38-46
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• 2005

To determine the actual hygienic status of domestic chicken meats sold in public markets (conventional markets and department stores), microbial contamination levels (Total cells, Coliforms and Staphylococcal cells) and zoonotic pathogens (Salmonella species, Campylobacter species, Listeria species, and Staphylococcus aureus) isolation tests were conducted. Chicken meats and eggs tested were collected from the conventional markets (Si-Jang) and department-stores located in Seoul and Kyung-gi regions in 1996. In total cells and coliforms contamination tests, chicken meats sold in department stores were much lesser contamination status than those of Si-Jang, but staphylococcal cells level was much more higher than that of conventional markets. Salmonella isolation frequency was investigated as $68.8\%$, but Campylobacter jejuni and Listeria monocytogenes isolation frequency were appeared both $64.0\%\;and\;63.3\%$. In case of eggs sold in public markets, one of S. gallinarum strain $(0.7\%)$ was isolated only on the egg-shell part among the four-hundred and fourty-six. In comparison with foreign imported chicken meats, there were no big differences in microbial contamination status. On the other hand, both Salmonella and L. monocytogenes were isolated only in the chicken wings from Korea and China, but not from U.S.A. This data suggest that more hygienic control system in order to produce the safe and hygienic chicken meats and eggs is need in our country as soon as possible.

• Health Policy and Management
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• v.10 no.3
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• pp.19-49
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• 2000

This study was conducted to help staff members of health centers manage personnel by examining the staff members' recognition level of organization structure of health centers, their motivation, their job satisfaction level and its related factors. Data were collected from 471 staff members of 14 health centers from March 3, 1999 to April 30, 1999. The results of this study are summarized as follows. In recognition levels of organization structure of health centers, the recognition level of necessity of discretion right was highest(3.55 points on the base of 5 points), while the recognition level of the location of decision making right was lowest(2.77 points). The general recognition of organization structure of health centers was 3.06 points, the suitability of division of duties was 3.05 points, and the optimum of manpower and budget was 2.93 points. The staff members' general recognition level of the organization structure appeared significantly higher in case of the groups of small and medium sized cities, above fifties, below high school graduate, above the sixth grade, public service experience of above 20 years, service period of below 2 years at present post, and average monthly salary of one million, eight hundred and ten thousand won. In the recognition level of the location of decision making right, the groups of big cities, male, the married, above the sixth grade, health and administration posts, average monthly salary of one million, three hundred and ten thousand won to one million, and eight hundred thousand won were significantly higher than the other groups. The recognition level of necessity of discretion right was higher in case of the groups of the twenties, the unmarried, above college graduate, nursing post, public service experience of below 5 years, service period of below 2 years at present post, and average monthly salary of below eight hundred thousand won. In the recognition level of suitability of division of duties, the groups of small and medium sized cities, the married, medical technicians, public service experience of above 20 years, and service period of below 4 years at present post were significantly higher than the other groups. In the staff members' recognition levels of organization management, the recognition level of opinion response when making decision was highest(2.92 points). The recognition level of rationality of the target amount establishment method was 2.88 points and the recognition level of personnel management was 2.63 points. The recognition level of personnel management was significantly higher in case of the groups of small and medium sized cities, the forties, above the sixth grade, medical technicians, public service experience of above 20 years, service period of below 2 years at present post, and average monthly salary of above one million, eight hundred and ten thousand won. In the recognition level of opinion response when making decision, the groups of small and medium sized cities, female, the eighth grade, health and administration posts, and service period of below 2 years at present post were higher than the other groups. The recognition level of rationality of the target amount establishment method was significantly higher in case of the groups of above fifties, below high school graduate, above the sixth grade, medical service post, and public service experience of 15 to 20 years. The factors significantly influencing sanitation were sex, education level, the period of public service experience, general recognition of organization structure, recognition of necessity of discretion right, recognition of suitability of division of duties, and recognition of opinion response when making decision. The factors which significantly influenced motivation were marital status, grade, recognition of the location of decision making right, recognition of necessity of discretion right, recognition of division of duties, recognition of opinion response when making decision, and sanitation. Sex, education level, recognition of suitability of division of duties, recognition of the target amount establishment method, and motivation influenced job satisfaction significantly. The factors significantly influencing organization culture were age, the period of public service experience, service period at present post, recognition of optimum of manpower and budget, recognition of suitability of division of duties, recognition of opinion response when making decision, and recognition of rationality of the target amount establishment method. In the coming days, the staff members' job satisfaction level must be increased through motivation and efficient conduct of duty must be accomplished through rational organization structure and management. Moreover, change of the staff members' consciousness and administrative system which are suitable for local autonomy system have to be established with increase of local residents' consciousness level and education level. Forming organization culture by reformative idea which fits the new era, public health service by the Community Health Act and health education service by the Health Promotion Act must be carried out efficiently. In doing so, financial support of central government and active efforts and concerns of local governments have to be devoted in order to get public health service in which peculiarity of the community is considered to be pursued well.