• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.85-107
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• 2024

This study analyzes trends in data curation research. A total of 1,849 scholarly records were extracted from Scopus and WoS, with 1,797 papers selected after removing duplicates. Titles, keywords, and abstracts were analyzed through keyword frequency analysis, LDA topic modeling, and network analysis. Frequent keywords like 'research' and 'information' suggest that data curation is widely applied in medical research, biomedical research, data management, and infrastructure. LDA modeling identified five main topics: improving medical data quality, enhancing big data management, managing scientific data and repositories, annotating and modeling medical data, and gene/protein database research. Network analysis showed that 'analysis' was central in global discussions, while 'gene' and 'system' were locally central. These findings highlight the importance of data curation in various research areas.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.770-802
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• 2022

With the development of deep learning techniques, text mining is producing breakthrough performance improvements, promising future applications, and practical use cases across many fields. Likewise, even though several attempts have been made in the field of financial information, few cases apply the current technological trends. Recently, companies and government agencies have attempted to conduct research and apply text mining in the field of financial information. First, in this study, we investigate various works using text mining to show what studies have been conducted in the financial sector. Second, to broaden the view of financial application, we provide a description of several text mining techniques that can be used in the field of financial information and summarize various paradigms in which these technologies can be applied. Third, we also provide practical cases for applying the latest text mining techniques in the field of financial information to provide more tangible guidance for those who will use text mining techniques in finance. Lastly, we propose potential future research topics in the field of financial information and present the research methods and utilization plans. This study can motivate researchers studying financial issues to use text mining techniques to gain new insights and improve their work from the rich information hidden in text data.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.66-85
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• 2020

The purpose of this study is to explain and predict trends the AI development process based on AI technology patents (total) and AI reporting frames in major newspapers. To that end, a summary of South Korean and U.S. technology patents filed over the past nine years and the AI (Artificial Intelligence) news text of major domestic newspapers were analyzed. In this study, Topic Modeling and Time Series Return Analysis using Big Data were used, and additional network agenda correlation and regression analysis techniques were used. First, the results of this study were confirmed in the order of artificial intelligence and algorithm 5G (hot AI technology) in the AI technical patent summary, and in the news report, AI industrial application and data analysis market application were confirmed in the order, indicating the trend of reporting on AI's social culture. Second, as a result of the time series regression analysis, the social and cultural use of AI and the start of industrial application were derived from the rising trend topics. The downward trend was centered on system and hardware technology. Third, QAP analysis using correlation and regression relationship showed a high correlation between AI technology patents and news reporting frames. Through this, AI technology patents and news reporting frames have tended to be socially constructed by the determinants of media discourse in AI development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2169-2190
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• 2022

The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.

• Journal of Korean Society of Rural Planning
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• v.27 no.3
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• pp.103-116
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• 2021

In this study, the characteristics of rural tourism image perceived by urban residents were analyzed through text analysis of blog data. In order to examine the images related to rural tourism, blog data written with the keyword "Gochang-gun travel" was used. LDA topic analysis, one of the text mining techniques, was used for the analysis. In the tourism image of Gochang-gun, 9 topics were derived, and 112 major places appeared. This was divided into 3 main activities and 5 object spaces through the review of keywords and the original text of blog data. As a result of the analysis, the traditional main resources of the region, Seonun mountain, Seonun temple, and Gochang-eup fortress, formed topic. On the other hand, world heritage such as dolmen and Ungok wetland did not appear as topic. In particular, the farms operated by the private sector form individual topics, and the theme farm can be seen as an important resource for tourism in Gochang-gun. Also, through the distribution of place keywords, it was possible to understand the characteristics of travel by region and the usage behavior of visitors. In the case of Gochang-gun, there was a phenomenon in which visitors were biased by region. This seems to be the result of Gochang-gun seeking to vitalize local tourism focusing on natural, ecological, and scenic resources. It is necessary to establish a plan for balanced regional development and develop other types of tourism resources. This study is different in that it identified the types and characteristics of rural tourism images in the region perceived by visitors, and the status of tourism at the regional level.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.28-37
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• 2020

This study was conducted to propose measures to improve crisis response to environmental issues by analyzing the news big data on the 'tap water larvae' situation and identifying related major keywords and topics. To accomplish this, 1,975 cases of 'tap water larvae' reported between July 13 to August 31, 2020 were divided into three periods and analyzed using topical modeling techniques. The analysis output 15 topics for each period. According to the result, the 'tap water larvae' incident, as reported in the media, is divided into the occurrence, diffusion, and rectification stages. The government's response and civilian risk consciousness and reaction could also be seen. Based on the result, the following measures to respond to environment risk is proposed. First, it is necessary to explore the various intertwined context with the 'tap water larvae' incident at its core and develop responsiveness to environmental problems through education which forms integrated views. Second, a role to monitor the environment must be implemented and civilian-participated environmental information must be shared through the application of internet communities. Third, the cultivation and deployment of environmental communicators who provide and communicate fast and accurate environment information is required. This study, as the first in Korea to use the topic modeling analysis method based on big data related to 'tap water larvae', has academic significance in that it has empirically and systematically analyzed environmental issues which appear as unstructured data. It also political significance as it suggests ways to improve environmental education and communication.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.83-93
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• 2001

Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.212-222
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• 2020

The purpose of this study is to understand the current status and trends of the research studies published by the Society for Industrial and Applied Mathematics which is a leader in the field of industrial mathematics around the world. To perform this purpose, titles and abstracts were collected from 6,255 research articles between 2016 and 2019, and the R program was used to analyze the topic modeling model with LDA techniques and a regression model. As the results of analyses, first, a variety of studies have been studied in the fields of industrial mathematics, such as algebra, discrete mathematics, geometry, topological mathematics, probability and statistics. Second, it was found that the ascending research subjects were fluid mechanics, graph theory, and stochastic differential equations, and the descending research subjects were computational theory and classical geometry. The results of the study, based on the understanding of the overall flows and changes of the intellectual structure in the fields of industrial mathematics, are expected to provide researchers in the field with implications of the future direction of research and how to build an industrial mathematics curriculum that reflects the zeitgeist in the field of education.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.285-307
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• 2019

In 2011, the Korean Medical Association revised the name of schizophrenia to remove the social stigma for the sick. Although it has been about nine years since the revision of the terminology, no studies have quantitatively analyzed how much social awareness has changed. Thus, this study investigates the changes in social awareness of schizophrenia caused by the revision of the disease name by analyzing Naver news articles related to the disease. For text analysis, LDA topic modeling, TF-IDF, word co-occurrence, and sentiment analysis techniques were used. The results showed that social awareness of the disease was more negative after the revision of the terminology. In addition, social awareness of the former term among two terms used after the revision was more negative. In other words, the revision of the disease did not resolve the stigma.

• The Journal of Information Systems
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• v.28 no.2
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• pp.153-179
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• 2019

Purpose This study collects the issues about work-life balance in Korea and United States and suggests the specific plans for work-life balance by the comparison and analysis. The objective of this study is to contribute to the improvement of people's life quality by understanding the concept of work-life balance that has become the issue recently and offering the detailed plans to be considered in respect of individual, corporate and governmental level for society of work-life balance. Design/methodology/approach This study collects work-life balance related issues through recruit sites in Korea and United States, compares and analyzes the collected data from the results of three text mining techniques such as LDA topic modeling, term frequency analysis and keyword extraction analysis. Findings According to the text mining results, this study shows that it is important to build corporate culture that support work-life balance in free organizational atmosphere especially in Korea. It also appears that there are the differences against whether work-life balance can be achieved and recognition and satisfaction about work-life balance along type of company or sort of working. In case of United States, it shows that it is important for them to work more efficiently by raising teamwork level among team members who work together as well as the role of the leaders who lead the teams in the organization. It is also significant for the company to provide their employees with the opportunity of education and training that enables them to improve their individual capability or skill. Furthermore, it suggests the roles of individuals, company and government and specific plans based on the analysis of text mining results in both countries.