• Journal of Information Technology Applications and Management
• /
• v.30 no.1
• /
• pp.53-70
• /
• 2023

Artificial intelligence has become familiar with modern society, not the distant future. As artificial intelligence and machine learning developed more highly and became more complicated, it became difficult for people to grasp its structure and the basis for decision-making. It is because machine learning only shows results, not the whole processes. As artificial intelligence developed and became more common, people wanted the explanation which could provide them the trust on artificial intelligence. This study recognized the necessity and importance of explainable artificial intelligence, XAI, and examined the trends of XAI research by analyzing social networks and analyzing topics with IEEE published from 2004, when the concept of artificial intelligence was defined, to 2022. Through social network analysis, the overall pattern of nodes can be found in a large number of documents and the connection between keywords shows the meaning of the relationship structure, and topic modeling can identify more objective topics by extracting keywords from unstructured data and setting topics. Both analysis methods are suitable for trend analysis. As a result of the analysis, it was found that XAI's application is gradually expanding in various fields as well as machine learning and deep learning.

• Journal of Research and Publication Ethics
• /
• v.2 no.1
• /
• pp.19-24
• /
• 2021

Purpose: The purpose of this study is to check the direction of KODISA's pursuit of complex and convergence studies by confirming the research trends of KODISA's direct academic journals such as JDS, JIDB, JBEES and JAFEB. To this end, we tried to compare and confirm the research trends of the papers in four academic journals targeting keywords. Research Design, data and methodology: The analysis was conducted from 2014 to 2020 on 867 papers from JDS, 315 papers from JIDB, 120 papers from JBEES, and 867 papers based on the publication year of the most recently published journal from JAFEB. For the analysis, frequency analysis, word crowding, topic modeling, and frequency analysis by applying weights for each year group were performed on the keywords crawled using Python. Results: The results of frequency analysis showed that each journal is properly oriented toward its target direction. In addition, it was confirmed that the results of topic modeling significantly reflected the results of frequency analysis. Finally, it could be concluded that the results of frequency analysis using the weights of keywords by year group were also developing in the direction the target journals were analyzed. Specifically, in the case of JDS, 20 keywords such as Service Quality, Distribution were found to increase continuously according to the year group. Meanwhile, the keywords that continued to increase according to JIDB's year group were India, Social Capital, and Job Stress. The keywords that continued to increase according to the year group of JBEES were Micro Finance Institutions and Microfinance, and the keywords that of JAFEB were confirmed to be Vietnam and Service Quality. Conclusion: It was confirmed that KODISA's direct management journals responded appropriately to convergence issues. In particular, it was confirmed that researches in various fields of JDS are continuously increasing. However, it seems that JIDB needs to deal with various issues additionally in the service industry field and JBEES in the environment field. Finally, it was found that JAFEB needs to be wary of the relatively low level of interest in some countries such as Kazakhstan and India in recent years.

• Journal of Korean Society for Quality Management
• /
• v.51 no.4
• /
• pp.701-713
• /
• 2023

Purpose: The primary purpose of this study is to integrate text mining and Quality Function Deployment (QFD) to automatically extract valuable information from customer reviews, thereby establishing a QFD frame- work to confirm genuine customer needs for New Product Development (NPD). Methods: Our approach combines text mining and QFD through topic modeling and sentiment analysis on a large data set of 56,873 customer reviews from Zappos.com, spanning five running shoe brands. This process objectively identifies customer requirements, establishes priorities, and assesses competitive strengths. Results: Through the analysis of customer reviews, the study successfully extracts customer requirements and translates customer experience insights and emotions into quantifiable indicators of competitiveness. Conclusion: The findings obtained from this research offer essential design guidance for new product develop- ment endeavors. Importantly, the significance of these results extends beyond the running shoe industry, presenting broad and promising applications across diverse sectors.

• Journal of Korean Society of Archives and Records Management
• /
• v.21 no.3
• /
• pp.163-186
• /
• 2021

The purpose of this study is to analyze research trends on overseas business archives, compare them with domestic and related prior studies, and present the direction of the development of domestic business archives research. For the research analysis, bibliography from 493 papers was collected using four academic databases: Web of Science, Scopus, LISA (ProQuest), LISS (EBSCOhost). Following data collection, data analysis was conducted and focused on the publication status of related papers, major journals, research forms, and researcher information. In addition, unique words were extracted through the data processing of English abstracts, and the frequency of the key words was identified. Through topic modeling, this study extracted seven topics from the entire literature group, analyzed the publication trends of the papers corresponding to each topic, and examined the topics and representative papers of that topic. The names for these topics were assigned as "access and usability of business records'," "information/evidence value of business records," "importance and operational strategy of business archives," "record management as a business heritage," "management of business records in digital environments," "preservation and disposal of business records," and "historical value of business records." Based on the prior research and analysis results, this study proposed implications for the development and expansion of future research on the domestic business archives.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.28 no.1
• /
• pp.191-207
• /
• 2017

This paper employs entitymetrics analysis on the research works of Dong-ju Yun. He was a Korean poet who was studied by many researchers on his works, religion and life. We collected 1,076 papers about Dong-ju Yun and conducted various approaches including co-author citation analysis, topic modeling analysis to identify the topic trend in the study of Dong-ju Yun. Also we extracted entities like person's name and literature's title from abstract to examine the relationship among them. The result of this paper enables us to objectively identify the topic trend and infer implicit relationships between key concept associated with Dong-ju Yun based on text data. Moreover, we observed sub-research topics such as life, poem, aesthetic existence, comparative literature, literary translation, and religious beliefs. This paper shows how entitymetrics can be utilized to study intellectual structures in the humanities.

• The Mathematical Education
• /
• v.59 no.1
• /
• pp.63-80
• /
• 2020

This study analyzed 3,114 articles published in KCI journals and 1,636 articles published in SSCI journals from 2000 to 2019 in order to compare domestic and international research trends of mathematics education using a topic modeling method. Results indicated that there were 16 similar research topics in domestic and international mathematics education journals: algebra/algebraic thinking, fraction, function/representation, statistics, geometry, problem-solving, model/modeling, proof, achievement effect/difference, affective factor, preservice teacher, teaching practice, textbook/curriculum, task analysis, assessment, and theory. Also, there were 7 distinct research topics in domestic and international mathematics education journals. Topics such as affective/cognitive domain and research trends, mathematics concept, class activity, number/operation, creativity/STEAM, proportional reasoning, and college/technology were identified from the domestic journals, whereas discourse/interaction, professional development, identity/equity, child thinking, semiotics/embodied cognition, intervention effect, and design/technology were the topics identified from the international journals. The topic related to preservice teacher was the most frequently addressed topic in both domestic and international research. The topic related to in-service teachers' professional development was the second most popular topic in international research, whereas it was not identified in domestic research. Domestic research in mathematics education tended to pay attention to the topics concerned with the mathematical competency, but it focused more on problem-solving and creativity/STEAM than other mathematical competencies. Rather, international research highlighted the topic related to equity and social justice.

• The Journal of Economics, Marketing and Management
• /
• v.11 no.3
• /
• pp.47-66
• /
• 2023

Purpose: The purpose of this study is to identify research trends related to ESG by domestic and overseas researchers so far, and to present research directions and clues for the possibility of applying ESG to Korean companies in the future and ESG practice through comparison of derived topics. Research design, data and methodology: In this study, as of October 20, 2022, after searching for the keyword 'ESG' in 'scienceON', 341 domestic papers with English abstracts and 1,173 overseas papers were extracted. For analysis, word frequency analysis, word co-occurrence frequency analysis, BERTopic, LDA, and OLS regression analysis were performed to confirm trends for each topic using Python 3.7. Results: As a result of word frequency analysis, It was found that words such as management, company, performance, and value were commonly used in both domestic and overseas papers. In domestic papers, words such as activity and responsibility, and in overseas papers, words such as sustainability, impact, and development were included in the top 20 words. As a result of analyzing the co-occurrence frequency of words, it was confirmed that domestic papers were related mainly to words such as company, management, and activity, and overseas papers were related to words such as investment, sustainability, and performance. As a result of topic modeling, 3 topics such as named ESG from the corporate perspective were derived for domestic papers, and a total of 7 topics such as named sustainable investment for overseas papers were derived. As a result of the annual trend analysis, each topic did not show a relatively increasing or decreasing tendency, confirming that all topics were neutral. Conclusions: The results of this study confirmed that although it is desirable that domestic papers have recently started research on consumers, the subject diversity is lower than that of overseas papers. Therefore, it is suggested that future research needs to approach various topics such as forecasting future risks related to ESG and corporate evaluation methods.

• Journal of Digital Contents Society
• /
• v.18 no.6
• /
• pp.1143-1150
• /
• 2017

The objective of this study is to examine the trends in information systems research. The abstracts of 1,245 articles were extracted from three leading Korean journals published between 2002 and 2016: Asia Pacific Journal of Information Systems, Information Systems Review, and The Journal of Information Systems. Time series analysis and topic modeling methods were implemented. The topic modeling results showed that the research topics were mainly "systems implementation", "communication innovation", and "customer loyalty". The time series regression results indicated that "customer satisfaction", "communication innovation", "information security", and "personal privacy" were hot topics, and on the other hand, "system implementation" and "web site" were the least popular. This study also provided suggestions for future research.

• Knowledge Management Research
• /
• v.23 no.3
• /
• pp.129-152
• /
• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

• Journal of the Korean Society for Library and Information Science
• /
• v.55 no.4
• /
• pp.141-157
• /
• 2021

In this study, we aimed to understand research trends of domestic artificial intelligence research. To achieve the goal, we applied network analysis and dynamic topic modeling to domestic research papers on artificial intelligence. Among the papers that have been indexed in KCI (Korean Journal of Citation Index) by 2020, metadata and abstracts of 2,552 papers where the titles or indexed keywords include 'artificial intelligence' both in Korean and English were collected. Keyword, affiliation, subject field, and abstract were extracted and preprocessed for further analyses. We identified main keywords in the field by analyzing keyword co-occurrence networks as well as the degree and characteristics of research collaboration between domestic and foreign institutions and between industry and university by analyzing institutional collaboration networks. Dynamic topic modeling was performed on 1845 abstracts written in Korean, and 13 topics were obtained from the labeling process. This study broadens the understanding of domestic artificial intelligence research by identifying research trends through dynamic topic modeling from abstracts as well as the degree and characteristics of research collaboration through institutional collaboration networks from author affiliation information. In addition, the results of this study can be used by governmental institutions for making policies in accordance with artificial intelligence era.