• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.1
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• pp.43-48
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• 2023

This study analyzed research trends in the field of ecological research. Data were collected based on a keyword search of the SCI, SSCI, and A&HCI databases from January 2002 to September 2022. The seven keywords, including biodiversity, ecology, ecotourism, species, climate change, ecosystem, restoration, wildlife, were recommended by ecological research experts. Word clouds were created for each of the searched keywords, and topic map analysis was performed. Topic map analysis using biodiversity, climate change, ecology, ecosystem, and restoration each generated 10 topics; topic maps analysis using the ecotourism keyword generated 5 topics; and topic map analysis using the wildlife keyword generated 4 topics. Each topic contained six keywords.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.141-148
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• 2016

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.

• Journal of the Korean Society of Costume
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• v.63 no.3
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• pp.138-150
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• 2013

The purpose of this study was to investigate research trends of subject matter in clothing construction field in clothing and textiles and to suggest the information for the future directions for fashion business and research. 2737 articles with clothing and textiles subject matter, 350 articles with clothing construction field in the Journal of Korean Society of Costume and Journal of the Korean Society of Clothing and Textiles from 2001 through 2010 were analyzed. The major conclusions of the study are as follows: 1. Clothing construction field took 12.8% with 350 articles in the researches of the Journal Publications in 2000s. 2. Clothing construction field showed more proportions in the latter half of the decade. 3. Clothing construction field were classified into 5 topics : topic of basic pattern and pattern for apparel, topic of body types, topic of functionality of clothing and protective clothing, topic of size system of apparel, topic of sewing and manufacturing process. 4. In clothing construction field, topic of basic pattern and pattern for apparel took the most proportions. 5. Topic of body types, topic of functionality of clothing and protective clothing, topic of size system of apparel, topic of sewing and manufacturing process were followed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.113-127
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• 2021

Topic modeling has been receiving much attention in academic disciplines in recent years. Topic modeling is one of the applications in machine learning and natural language processing. It is a statistical modeling procedure to discover topics in the collection of documents. Recently, there have been many attempts to find out topics in diverse fields of academic research. Although the first Department of Industrial Engineering (I.E.) was established in Hanyang university in 1958, Korean Institute of Industrial Engineers (KIIE) which is truly the most academic society was first founded to contribute to research for I.E. and promote industrial techniques in 1974. Korean Society of Industrial and Systems Engineering (KSIE) was established four years later. However, the research topics for KSIE journal have not been deeply examined up until now. Using topic modeling algorithms, we cautiously aim to detect the research topics of KSIE journal for the first half of the society history, from 1978 to 1999. We made use of titles and abstracts in research papers to find out topics in KSIE journal by conducting four algorithms, LSA, HDP, LDA, and LDA Mallet. Topic analysis results obtained by the algorithms were compared. We tried to show the whole procedure of topic analysis in detail for further practical use in future. We employed visualization techniques by using analysis result obtained from LDA. As a result of thorough analysis of topic modeling, eight major research topics were discovered including Production/Logistics/Inventory, Reliability, Quality, Probability/Statistics, Management Engineering/Industry, Engineering Economy, Human Factor/Safety/Computer/Information Technology, and Heuristics/Optimization.

• Korea Trade Review
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• v.45 no.6
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• pp.157-179
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• 2020

This study presented the results of meta-analysis through topic modeling among the papers published in the Journal of the International Trade Association for the purpose of presenting academic research trends in the field of trade insurance and future research directions. Among the total 2,010 papers included in the Journal of the Korea International Trade Association, the analyzed paper covers the subject of trade-related insurance. According to detailed topics, 33 marine insurance (42.31%), 16 export insurance (20.51%), 11 hull insurance (14.10%), and 18 others (23.08%), and 4 other products liability insurance. According to the empirical analysis results, Topic 1 was classified as marine insurance, airworthiness, notice obligation, and collateral, and Topic 2 was derived as a representative topic for loading insurance, emergency risk, and immunity as export insurance. And Topic 3 was classified as vessel, sinking and container in relation to ship insurance, and Topic 4 was analyzed as an important topic such as manufacture and British marine insurance. Through the analysis results, we selected the representative topic used for the trade insurance topic and looked at the status of major research. Trade insurance is an area that requires the development of more theoretical and practical research subjects as an optimal risk management means in international trade transactions. To this end, first, support from the Korea International Trade Association is needed to establish a continuous research subject sharing system for the development of research subjects in the field of trade insurance. Second, academic journal operation management must be continuously managed in which academic research papers can be submitted and published.

• The Journal of Information Systems
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• v.31 no.4
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• pp.25-45
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• 2022

Purpose In this paper, we propose the method to extend topic modeling techniques in order to derive data-based research hypotheses when establishing research hypotheses for social sciences, As a concept in contrast to the existing deductive hypothesis establishment methodology for the social science research, the topic modeling technique was expanded to enable the so-called inductive hypothesis establishment methodology, and an analysis case of the Seongsan Ilchulbong online review based on the proposed methodology was presented. Design/methodology/approach In this paper, an extension architecture and extension algorithm in the form of extending the existing topic modeling were proposed. The extended architecture and algorithm include data processing method based on topic ratio in document, correlation analysis and regression analysis of processed data for topics derived by existing topic modeling. In addition, in this paper, an analysis case of the online review of Seongsan Ilchulbong Peak was presented by applying the extended topic modeling algorithm. An exploratory analysis was performed on the Seongsan Ilchulbong online reviews through the basic text analysis. The data was transformed into 5-point scale to enable correlation and regression analysis based on the topic ratio in each online review. A regression analysis was performed using the derived topics as the independent variable and the review rating as the dependent variable, and hypotheses could be derived based on this, which enable the so-called inductive hypothesis establishment. Findings This paper is meaningful in that it confirmed the possibility of deriving a causal model and setting an inductive hypothesis through an extended analysis of topic modeling.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.423-432
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• 2022

This paper analyzes a total of 10,599 papers related to COVID-19 from January 2020 to July 2022 collected from the KCI site using LDA topic modeling so that academic researchers can understand the overall research trend. The results of LDA topic modeling are analyzed by major research categories so that academic researchers can easily figure out topics in their research fields. Then, the detailed research category information in which a lot of research is done by topic is analyzed. It is very important for academic researchers to understand the trend of research topics over time. Therefore, in this paper, the trend of topics is analyzed and presented using time series decomposition.

• Journal of Korean society of Dental Hygiene
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• v.22 no.6
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• pp.495-502
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• 2022

Objectives: The purpose of this study was to analyze research trends in dental hygiene using topic modeling and semantic network analysis. Methods: A total of 261 published studies were collected 686 key words from the Research Information Sharing Service (RISS) by 2019-2021. Topic modeling and semantic network analysis were performed using Textom. Results: The most frequently and frequency-inverse document frequently key words were 'dental hygienist', 'oral health', 'elderly', 'periodontal disease', 'dental hygiene'. N-gram of key words show that 'dental hygienist-emotional labor', 'dental hygienist-elderly', 'dental hygienist-job performance', 'oral health-quality of life', 'oral health-periodontal disease' etc. were frequently. Key words with high degree centrality were 'dental hygienist (0.317)', 'oral health (0.239)', 'elderly (0.127)', 'job satisfaction (0.057)', 'dental care (0.049)'. Extracted topics were 5 by topic modeling. Conclusions: Results from the current study could be available to know research trends in dental hygiene and it is necessary to improve more detailed and qualitative analysis in follow-up study.

• Korean Medical Education Review
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• v.24 no.2
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• pp.93-102
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• 2022

We use the concept of the "doctor's role" as a guideline for developing medical education programs for medical students, residents, and doctors. Therefore, we should regularly reflect on the times and social needs to develop a clear sense of that role. The objective of the present study was to understand the knowledge structure related to doctor's job competencies in Korea. We analyzed research trends related to doctor's job competencies in Korea Citation Index journals using text network analysis through an integrative approach focusing on identifying social issues. We finally selected 1,354 research papers related to doctor's job competencies from 2011 to 2020, and we analyzed 2,627 words through data pre-processing with the NetMiner ver. 4.2 program (Cyram Inc., Seongnam, Korea). We conducted keyword centrality analysis, topic modeling, frequency analysis, and linear regression analysis using NetMiner ver. 4.2 (Cyram Inc.) and IBM SPSS ver. 23.0 (IBM Corp., Armonk, NY, USA). As a result of the study, words such as "family," "revision," and "rejection" appeared frequently. In topic modeling, we extracted five potential topics: "topic 1: Life and death in medical situations," "topic 2: Medical practice under the Medical Act," "topic 3: Medical malpractice and litigation," "topic 4: Medical professionalism," and "topic 5: Competency development education for medical students." Although there were no statistically significant changes in the research trends for each topic over time, it is nonetheless known that social changes could affect the demand for doctor's job competencies.