• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.1
• /
• pp.1-7
• /
• 2021

Investor mood from social media is gaining increasing attention for leading a price movement in stock market. Based on the behavioral finance theory, this study argues that sentiment extracted from social media using big data technique can predict a real-time (short-run) price momentum in Chinese stock market. Collecting Sina Weibo posts that related to COVID-19 using keyword method, a daily influential weighted sentiment factors is extracted from the sizable raw data of over 2 millions of posts. We examine one supervised and 4 unsupervised sentiment analysis model, and use the best performed word-frequency and BiLSTM mdoel. The test result shows a similar movement between stock price change and sentiment factor. It indicates that public mood extracted from social media can in some extent represent the investors' sentiment and make a difference in stock market fluctuation when people are concentrating on a special events that can cause effect on the stock market.

• Journal of Korean Library and Information Science Society
• /
• v.52 no.1
• /
• pp.55-77
• /
• 2021

Based on the analysis of historical backgrounds and terms of Taekwondo, this study was conducted to analyze the characteristics of cataloging records related to Taekwondo in 53 national libraries of each country. The results are as follows. To begin with, while most of the Taekwondo-related records are concentrated in some specific national libraries such as the United States, Germany, Republic of China, United Kingdom, and Spain, there are four libraries that do not have one. Second, the title keyword of Taekwondo-related records was 93.5% for the term that directly meant Taekwondo and 6.5% for Korean martial art, Korean art of self-defense, and Korean karate etc. The frequency of materials by language is 38.7% for English and 8~9% for German, Spanish, Chinese, and Korean, respectively. The Roman translation for Taekwondo is 50.3% for 'Taekwondo', and 18.5% for 'Tae kwon do'. Third, the subject heading of Taekwondo-related records was 86.9% for 'Tae kwon do' or 'Taekwondo' etc. 7.6% for 'karate', 5.7% for general subject heading, and 12.0% for blank. This means that some national libraries misunderstand Taekwondo as karate.

• The Journal of the Korea Contents Association
• /
• v.21 no.8
• /
• pp.662-674
• /
• 2021

Metabolic syndrome is closely associated with cardiovascular disease, there is increasing attentions in prevention of metabolic syndrome through prediction. The aim of this study was to systematically review the literature by collecting, analyzing, and synthesizing articles of predicting metabolic syndrome in Koreans. For systemic review, data search was conducted on Global journals Pubmed, WoS and domestic journals DBPia, KISS published in 2011-2020 year. Three keyword 'Metabolic syndrome', 'predict', and 'korea' were used for searching under AND condition. Total 560 articles were searched and the final 22 articles were selected according to the data selection criteria. The most useful variable was WHtR(AUC=0.897), most frequently used analysis method was logistic regression(63.6%), and most accurate analysis method was XGBOOST(AUC=0.879) for predicting metabolic syndrome. Prediction accuracy was slightly improved when sasang constitution types was used. Based on the results of this study, it is believed that various large-scale longitudinal studies for the prediction and management of the Metabolic syndrome in Korean should be followed in the future.

• The Journal of the Korea Contents Association
• /
• v.21 no.11
• /
• pp.64-76
• /
• 2021

Culture technology (CT) becomes important in the recent environment where digital technology drives content-based innovations. However, technological trends of CT have not been systematically discussed. Especially, the trends of CT should be analyzed from the national perspective, because CT has grown with the help of government-driven innovation. Therefore, this paper aims to analyze CT trends focusing on national research projects. We collected data on CT from the national science and technology information service (NTIS) database, analyzed the keyword co-occurrence network, and identified the patterns of technological innovation using a clustering analysis. As a result, we found that CT has contributed to the digital content and cultural media, and has been actively developed with the help of machine learning technique. Especially, due to the rise of Covid-19, the non-face-to-face online content is rapidly increasing. This study provides important clues for understanding, analyzing CT trends.

• Journal of The Korean Society of Integrative Medicine
• /
• v.11 no.3
• /
• pp.171-183
• /
• 2023

Purpose : This study sought to investigate research trends regarding joint mobilization type among patients with shoulder joint diseases. Methods : A scoping review was conducted according to the five steps outlined by Arskey and O'Malley and PRISMA-ScR. We searched six domestic databases (ScienceOn, DBpia, Riss, Kmbase, Kiss, KCI) and three international databases (CINAHL, Pubmed, Cochrane central) between 2013 and June 2023. The keyword terms used were 'joint mobilization', 'Kaltenborn', 'Maitland', 'Mulligan', and 'shoulder joint'. Results : There were a total of 44 studies that investigated the topic, and these were divided into quantitative analysis and topic analysis. In terms of publication year, the number of studies within the last five years has increased more than compared to the previous five years, with most of them being randomized clinical trials. In shoulder joint diseases, it was found that the majority of joint movement studies focused on adhesive joint cystitis and shoulder collision syndrome. The Mulligan concept was the most commonly studied type of joint motion. The dependent variables used included pain, joint function (disability), and muscle function. The visual analog scale was the most commonly used for the pain variable, followed by the numeric rating scale. For joint function and disability variables, range of motion was the most commonly used, followed by shoulder pain and disability index, and disabilities of the arm, shoulder, and hand. For muscle function, variables such as muscle tone, strength, and activity were used. Conclusion : We believe that findings of this scoping review can serve as valuable mapping data for joint mobilization research on shoulder joint diseases. Further studies including systematic reviews and meta-analyses based on these results are recommended.

• Smart Media Journal
• /
• v.12 no.6
• /
• pp.27-34
• /
• 2023

As Netflix usage has increased due to the COVID-19 pandemic, users' experiences with the service have also increased. Therefore, this study aims to conduct topic modeling analysis based on Netflix review data to explore the changes in Netflix user experience and service before and after the COVID-19 pandemic. We collected Netflix app review data from the Google Play Store using the Google Play Scraper library, and used topic modeling to examine keyword differences between app reviews before and after the pandemic. The analysis revealed four main topics: Netflix app features, Netflix content, Netflix service usage, and Netflix overall reviews. After the pandemic, when user experience increased, users tended to use more diverse and detailed keywords in their reviews. By using Netflix review data to analyze users' opinions, this study shows the changes in user experience of Netflix services before and after the pandemic, which can be used as a guide to strengthen competitiveness in the competitive OTT market.

• The Journal of Engineering Geology
• /
• v.31 no.3
• /
• pp.307-319
• /
• 2021

Although it is currently conducting slope management and research using digital technologies such as drones, big data, and artificial intelligence, it is still somewhat insufficient and is still vulnerable to slope failure. For this reason, it is inevitable to present the development direction for research on prediction and analysis of slope failure using the digital technologies to effectively deal with slope failure, which requires a preemptive understanding of prediction and analysis of slope failure. In this paper, we collected literature data based on the Web of Science for five years from January 1, 2016 to December 31, 2020 and analyzed by co-word analysis to identify the domain structure of research on prediction and analysis of slope failure. Detailed subject areas were identified through network analysis, and the domain relationships between keywords were visualized to derive global and regionally oriented keywords through relationship, centrality analysis. In addition, the clusters formed by performing cluster analysis were displayed on the multidimensional scailing map, and the domain structure according to the correlation between each keyword was presented. The results of this study reveal the domain structure of research on prediction and analysis of slope failure, and are expected to be usefully used to find future research directions.

• Journal of Internet Computing and Services
• /
• v.16 no.2
• /
• pp.57-66
• /
• 2015

Considerable research efforts are being directed towards analyzing unstructured data such as text files and log files using commercial and noncommercial analytical tools. In particular, researchers are trying to extract meaningful knowledge through text mining in not only business but also many other areas such as politics, economics, and cultural studies. For instance, several studies have examined national pending issues by analyzing large volumes of text on various social issues. However, it is difficult to provide successful information services that can identify R&D documents on specific national pending issues. While users may specify certain keywords relating to national pending issues, they usually fail to retrieve appropriate R&D information primarily due to discrepancies between these terms and the corresponding terms actually used in the R&D documents. Thus, we need an intermediate logic to overcome these discrepancies, also to identify and package appropriate R&D information on specific national pending issues. To address this requirement, three methodologies are proposed in this study-a hybrid methodology for extracting and integrating keywords pertaining to national pending issues, a methodology for packaging R&D information that corresponds to national pending issues, and a methodology for constructing an associative issue network based on relevant R&D information. Data analysis techniques such as text mining, social network analysis, and association rules mining are utilized for establishing these methodologies. As the experiment result, the keyword enhancement rate by the proposed integration methodology reveals to be about 42.8%. For the second objective, three key analyses were conducted and a number of association rules between national pending issue keywords and R&D keywords were derived. The experiment regarding to the third objective, which is issue clustering based on R&D keywords is still in progress and expected to give tangible results in the future.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
• /
• v.36 no.2
• /
• pp.60-75
• /
• 2023

Objectives : A bibliometric approach using network analysis was applied to explore the global trends of research on herbal medicine for atopic treatment. Methods : Articles related to herbal medicine on atopic treatment from 2003 to 2022 were retrieved from Web of Science Core Collection. Extracted records were analyzed according to the publication year, research area, journal title, country, organization, author and keyword. The VOSviewer program was used to visualize the trends and the research hotspots in herbal medicine for atopy. Results : Analysis of 406 articles indicated the consistent increase of using herbal medicine for atopic treatment over the last 20 years. The most productive country and research organization in issuing articles were South Korea and Kyunghee university. Many articles have been published in research areas such as 'integrative complementary medicine' and 'pharmacology pharmacy'. By evaluating the total link strength, the average publication year and the average citations of countries and authors, the influential countries and authors were identified. A network analysis based on the co-occurrence and the publication year of keywords revealed the relevant characteristics and trends of herbal medicine for atopy. The most up-to-date keywords were 'topical application', 'skin barrier' and 'care'. Conclusions : This bibliometric study examined the overall trends and the time-based development of herbal medicine for atopic treatment. The current study would be useful not only for grasping the global network hub of research on herbal medicine for atopic treatment, but also to explore the new directions for future research.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.123-139
• /
• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.