• Journal of Korean Institute of Industrial Engineers
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• v.42 no.6
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• pp.421-431
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• 2016

Over the last four decades, industrial engineering (IE) research in Korea has continued to evolve and expand to respond to social needs. This paper aims to identify research topics in IE research and explore their dynamic changes over time. The topic modeling approach, which automatically discovers topics that pervade a large and unstructured collection of documents, is adopted to identify research topics in domestic IE research. 1,242 articles published from 2001 to 2015 in two IE journals issued by the Korean Institute of Industrial Engineers were collected and their English abstracts were analyzed. Applying the Latent Dirichlet Allocation model led us to uncover 50 topics of domestic IE research. The top 10 most popular topics are revealed, and topic trends are explored by examining the dynamic changes over time. The four topics, technology management, financial engineering, data mining (supervised learning), efficiency analysis, are selected as hot topics while several traditional topics related with manufacturing are revealed as cold topics. The findings are expected to provide fruitful implications for IE researchers.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.81-92
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• 2023

The purpose of this study is to identify rural research topics, differences in research topics over time, and key mediators through the analysis of academic research trends using topic modeling. This study analyzed a total of 1,183 articles published in the Journal of Rural Planning and Rural Society over a 23-year period (2000-2022). We categorized rural research topics into 30, examined the proportion of research in each topic, and identified major changes in research topics over time. We also identified key words that mediate between research topics. The study found that, first, rural research trends can be categorized into five types (resources and utilization, area/space, people, ecosystem/environment, and tourism), with area/space being the most studied. Subtopics include rural amenities, rural disappearance/village miniaturization, and rural landscape management. Second, the research topics for each period were different. In the first period(2003-2007), the main research topics were rural amenities and Agricultural production- based climate vulnerability assessment. In the second period(2008-2012), the main research topics were Rural extinction and village depopulation, and rural landscape management, and in the third period(2013-2017), the main research topics were rural sixth industrialization and rural ecotourism. In the fourth period(2018-2022), rural development planning and rural life services(life SOC) were the main research topics. The significance of this study is that it extends the existing method of analyzing research trends and provides basic data to enhance comprehensive insights and understanding of rural research.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.15-33
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• 2018

The main purpose of this study was to identify the topics and research trends of international cadastral research using LDA. To achieve this goal, I reviewed the literature on LDA and international cadastral study and formulated four research questions that are topics of cadastral researchers, distribution of topics, the most influential topics and changes of topics over time. To answer these research questions, I analyzed 370 papers published in the FIG Peer Review Journal between January 1, 2008, and October 31, 2017, using LDA. As a result of the analysis, I confirmed that there are twelve major topics in international cadastral research. And the most influential topic of these topics was identified as topic 2(cadastral information systems), and topic 5(land development and land administration) was also confirmed as playing an important role in the overall document. These two topics have been the most popular topics whose trendlines have been very active over the past decade and will play a leading role in future cadastral research.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.6
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• pp.1894-1906
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• 2016

The purpose of this study is to analyze the setting and directions of cross-curricular learning topics based on research on experts' recognition of cross-curricular learning topics. The study method adopted was Delphi, and the subjects selected were curricular experts. This study has drawn following results: first, regarding the essence and problems of cross-curricular learning topics, even among the experts, there is no opinion agreed about cross-curricular learning topics' concept, essence, or characters. Second, more detailed discussion is demanded to select cross-curricular learning topics and set up a guideline about the operation. Third, it is needed to examine closely if presently suggested cross-curricular learning topics are duplicated or not and consider related subjects connected with those cross-curricular learning topics to improve education more systematically. Fourth, it is necessary to conduct more profound and systematic research on core competence that can embrace those cross-curricular learning topics. Fifth, to cope with changes in society and demands at school, it is needed to discuss how cross-curricular learning topics should be added or which learning topics should be added.

• Korea Trade Review
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• v.45 no.3
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• pp.55-69
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• 2020

This study examines the research trends and knowledge structure of international trade studies using topic modeling method, which is one of the main methodologies of text mining. We collected and analyzed English abstracts of 1,868 papers of three Korean major journals in the area of international trade from 2003 to 2019. We used the Latent Dirichlet Allocation(LDA), an unsupervised machine learning algorithm to extract the latent topics from the large quantity of research abstracts. 20 topics are identified without any prior human judgement. The topics reveal topographical maps of research in international trade and are representative and meaningful in the sense that most of them correspond to previously established sub-topics in trade studies. Then we conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics. We discovered 2 hot topics(internationalization capacity and performance of export companies, economic effect of trade) and 2 cold topics(exchange rate and current account, trade finance). Trade studies are characterized as a interdisciplinary study of three agendas(i.e. international economy, International Business, trade practice), and 20 topics identified can be grouped into these 3 agendas. From the estimated results of the study, we find that the Korean government's active pursuit of FTA and consequent necessity of capacity building in Korean export firms lie behind the popularity of topic selection by the Korean researchers in the area of int'l trade.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.47-65
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• 2021

This study aims to identify specific topics, trends, and structural characteristics of scholarly communication research, based on 1,435 articles published from 1970 to 2018 in the Scopus database through Latent Dirichlet Allocation topic modeling, serial analysis, and network analysis. Topic modeling, time series analysis, and network analysis were used to analyze specific topics, trends, and structures, respectively. The results were summarized into three sets as follows. First, the specific topics of scholarly communication research were nineteen in number, including research resource management and research data, and their research proportion is even. Second, as a result of the time series analysis, there are three upward trending topics: Topic 6: Open Access Publishing, Topic 7: Green Open Access, Topic 19: Informal Communication, and two downward trending topics: Topic 11: Researcher Network and Topic 12: Electronic Journal. Third, the network analysis results indicated that high mean profile association topics were related to the institution, and topics with high triangle betweenness centrality, such as Topic 14: Research Resource Management, shared the citation context. Also, through cluster analysis using parallel nearest neighbor clustering, six clusters connected with different concepts were identified.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.1-7
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• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.50-59
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• 2021

To understand the research trends of English education in Korea for the last 20 years from 2000 to 2019, 12 major academic journals in Korea in the field of English education were selected, and bibliographic information of 7,329 articles published in these journals were collected and analyzed. The total number of articles increased from the 2000s to the first half of the 2010s, but decreased somewhat in the late 2010s and the number of publications by journal has become similar. These results show that the overall influence of English education journals has decreased and then leveled in terms of quantity. Next, 34 topics were extracted by applying latent Dirichlet allocation (LDA) topic modeling using the English abstract of the articles. Teacher, word, culture/media, and grammar appeared as topics that were highly studied. Topics such as word, vocabulary, and testing and evaluation appeared through unique keywords, and various topics related to learner factors emerged, becoming topics of interest in English education research. Then, topics were analyzed to determine which ones were rising or falling in frequency. As a result of this analysis, qualitative research, vocabulary, learner factor, and testing were found to be rising topics, while falling topics included CALL, language, teaching, and grammar. This change in research topics shows that research interests in the field of English education are shifting from static research topics to data-driven and dynamic research topics.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1096-1107
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• 2019

In this paper, we present an ontology-based approach to labeling influential topics of scientific articles. First, to look for influential topics from scientific article, topic modeling is performed, and then social network analysis is applied to the selected topic models. Abstracts of research papers related to data mining published over the 20 years from 1995 to 2015 are collected and analyzed in this research. Second, to interpret and to explain selected influential topics, the UniDM ontology is constructed from Wikipedia and serves as concept hierarchies of topic models. Our experimental results show that the subjects of data management and queries are identified in the most interrelated topic among other topics, which is followed by that of recommender systems and text mining. Also, the subjects of recommender systems and context-aware systems belong to the most influential topic, and the subject of k-nearest neighbor classifier belongs to the closest topic to other topics. The proposed framework provides a general model for interpreting topics in topic models, which plays an important role in overcoming ambiguous and arbitrary interpretation of topics in topic modeling.

• Journal of the Korean Society of Food Culture
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• v.37 no.3
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• pp.179-195
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• 2022

For any research study, in order to achieve the researcher's intended purpose, the depth of research is added, and the area of the subject is expanded by clearly defining the scope and objective. The study was undertaken to analyze the bibliographic data of 254 papers in the field of foodservice and restaurant published in the Journal of the Korean Dietary Culture from 2002 to 2021. The study was divided into two periods: 2002 to 2011, and 2012 to 2021. Research topics were derived and research trends according to temporal changes were confirmed through analysis of keyword networks by period. In addition, analyzing the keyword network of simultaneous appearance of "foodservice" and "restaurant", the research topics were compared and analyzed in relation to which keywords were expanded by period. Our analysis revealed that the research topics were mostly studied for satisfaction and nutrition. Additionally, they were classified into procurement, Korean food before employee menu, marketing, restaurant industry, and quality. In the period from 2002 to 2011, it was confirmed that studies encompassed a wide range of research topics, focusing on foodservice and restaurant; in the second period from 2012 to 2021, the research topics were more classified and subdivided.