• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.64-73
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• 2015

Recent emergence of spatial big data attracts the attention of various research groups. This paper analyzes the research trend on spatial big data by text mining the related Scopus DB. We apply topic model and network analysis to the extracted abstracts of articles related to spatial big data. It was observed that optics, astronomy, and computer science are the major areas of spatial big data analysis. The major topics discovered from the articles are related to mobile/cloud/smart service of spatial big data in urban setting. Trends of discovered topics are provided over periods along with the results of topic network. We expect that uncovered areas of spatial big data research can be further explored.

• Journal of the Microelectronics and Packaging Society
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• v.27 no.2
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• pp.53-58
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• 2020

Analysis of electric semiconductor packaging technology for electric vehicles was performed. Topic modeling using LDA technique was performed by collecting valid patents by deriving valid patents. It was classified into 20 topics, and the definition of technology was defined through extracted words for each topic. In order to analyze the trend of each topic, the trend of power semiconductor packaging technology was analyzed by deriving hot and cold topics by topic through regression analysis on frequency by year. The package structure technology according to the withstand voltage, the input/output-related control technology and the heat dissipation technology were derived as the hot topic technology, and the inductance reduction technology was derived as the cold topic technology.

• Journal of Wellbeing Management and Applied Psychology
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• v.5 no.4
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• pp.33-37
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• 2022

Purpose: This study aims to understand the trend and central concept of domestic researches on laughter therapy. For the analysis, this study used total 72 theses verified by inputting the keyword 'laughter therapy' from 2007 to 2021. Research design, data and methodology: This study performed the development and analysis of keyword co-occurrence network, analyzed the types of researches through topic modeling, and verified the visualized word cloud and sociogram. The keyword data that was cleaned through preprocessing, was analyzed in the method of centrality analysis and topic modeling through the 1-mode matrix conversion process by using the NetMiner (version 4.4) Program. Results: The keywords that most appeared for last 14 years were laughter therapy, depression, the elderly, and stress. The five topics analyzed in thesis data from 2007 to 2021 were therapy, cognitive behavior, quality of life, stress, and the elderly. Conclusions: This study understood the flow and trend of research topics of domestic laughter therapy for last 14 years, and there should be continuous researches on laughter therapy, which reflects the flow of time in the future.

• Journal of the Korean Society for information Management
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• v.30 no.1
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• pp.285-302
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• 2013

This study identified topic shifts and patterns over time by analyzing an enormous amount of Twitter data whose characteristics are high accessibility and briefness. First, we extracted keywords for a certain product and used them for representing the topic network allows for intuitive understanding of keywords associated with topics by nodes and edges by co-word analysis. We conducted temporal analysis of term co-occurrence as well as topic modeling to examine the results of network analysis. In addition, the results of comparing topic shifts on Twitter with the corresponding retrieval results from newspapers confirm that Twitter makes immediate responses to news media and spreads the negative issues out quickly. Our findings may suggest that companies utilize the proposed technique to identify public's negative opinions as quickly as possible and to apply for the timely decision making and effective responses to their customers.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.2
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• pp.175-185
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• 2021

Purpose: The aim of this study was to identify core keywords and topic groups in the 'Gestational diabetes mellitus (GDM) and Breastfeeding' field of research for better understanding research trends in the past 20 years. Methods: This was a text-mining and topic modeling study composed of four steps: 1) collecting abstracts, 2) extracting and cleaning semantic morphemes, 3) building a co-occurrence matrix, and 4) analyzing network features and clustering topic groups. Results: A total of 635 papers published between 2001 and 2020 were found in databases (Web of Science, CINAHL, RISS, DBPIA, RISS, KISS). Among them, 3,639 words extracted from 366 articles selected according to the conditions were analyzed by text network analysis and topic modeling. The most important keywords were 'exposure', 'fetus', 'hypoglycemia', 'prevention' and 'program'. Six topic groups were identified through topic modeling. The main topics of the study were 'cardiovascular disease' and 'obesity'. Through the topic modeling analysis, six themes were derived: 'cardiovascular disease', 'obesity', 'complication prevention strategy', 'support of breastfeeding', 'educational program' and 'management of GDM'. Conclusion: This study showed that over the past 20 years many studies have been conducted on complications such as cardiovascular diseases and obesity related to gestational diabetes and breastfeeding. In order to prevent complications of gestational diabetes and promote breastfeeding, various nursing interventions, including gestational diabetes management and educational programs for GDM pregnancies, should be developed in nursing fields.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1697-1704
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• 2017

As the power grid has been changed to a smart grid, existing power technologies are evolving into convergence technology through interdisciplinary research. According to the government policy to increase the proportion of renewable energy to 20% by 2030, the speed seems to be accelerating. This study analyzes the relationship between research technologies in order to grasp research trends of smart grid technology. To this end, we analyze the relationship between keywords extracted from topic modeling using social network analysis methodology. This is because, in the field where interdisciplinary research such as smart grid is active, each research topic is not independent, but research technologies emerging in one topic coexist in different topics, and linkage between research technologies can be important information. Therefore, this study can contribute to the analysis of research trend as it can be used as a package tool together with a topic modeling methodology.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.55-65
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• 2023

In this paper, we apply KeyBERT(Keyword extraction with Bidirectional Encoder Representations of Transformers) algorithm-driven topic extraction and topic frequency analysis to deep learning and reinforcement learning research to discover the rapidly changing trends in them. First, we crawled abstracts of research papers on deep learning and reinforcement learning, and temporally divided them into two groups. After pre-processing the crawled data, we extracted topics using KeyBERT algorithm, and then analyzed the extracted topics in terms of topic occurrence frequency. This analysis reveals that there are distinct trends in research work of all analyzed algorithms and applications, and we can clearly tell which topics are gaining more interest. The analysis also proves the effectiveness of the utilized topic extraction and topic frequency analysis in research trend analysis, and this trend analysis scheme is expected to be used for research trend analysis in other research fields. In addition, the analysis can provide insight into how deep learning will evolve in the near future, and provide guidance for select research topics and methodologies by informing researchers of research topics and methodologies which are recently attracting attention.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.73-80
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• 2022

In this study, prior studies of 280 small business owners in Korea over the past two decades were comprehensively analyzed through keyword network and LDA topic modeling analysis, and overall views and trends in academia were examined. As core keywords, "sales" and "protection," which conflict with each other but are essential for stable and sustainable growth were selected, and 7 topics (Topic 1: start-up, topic 2: digital, topic 3: tax system, topic 4: capability, topic 5: coexistence, topic 6: regulation, and topic 7: funding) were drawn up. Based on the results of the analysis, the need to improve digital maturity for the continued growth and development of small business owners was raised, and the response at the pan-ministerial level and the stability of the performance of functions that can survive even after the new administration to solve the economic damage problems facing small business owners were suggested. In addition, attention to the long-term, speed, detail, and direction of government support in a new way, and a flexible approach to the negative way in which pre-allowance and post-regulation is given were suggested.

• Journal of the Korean Society for information Management
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• v.30 no.1
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• pp.237-258
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• 2013

As topics of researchers become diverse horizontally or vertically, academic libraries have difficulties to identify the dynamic change of researchers' needs for academic publications. This research aims to illustrate the topic areas of researchers in a department of university by analyzing bibliographies of their publications. First, researchers' publications were used to discover the topic areas where the researchers had published. Second, the cited publications in those papers were analysed to identify the expanded topic areas of these researchers. Finally, highly cited journals were analyzed by network analysis method. The major finding is that the importance of topic areas by the number of journals was not necessarily proportional to that by the number of papers. Researchers have a tendency to use many papers in a small number of journals in a certain topic area. Furthermore, the importance of topic areas discovered by researchers' publications was not the same as that discovered by researchers' citations.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.873-885
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• 2022

The particulate matter (PM) has emerged as a hot topic around the world as it has been reported that PM is related to an increase in mortality and prevalence rates. In South Korea, the importance of PM has been recognized since the late 1990s, and various studies on PM have been conducted. This study investigated the PM research topics and trends for papers (D=2,764) published in Research Information Sharing Service (RISS) using topic modeling based on Latent Dirichlet Allocation (LDA). As a result, a total of 10 topics were identified in the whole papers, and the PM research topics were classified as 'PM reduction (Topic 1)', 'Government policy and management (Topic 2)', 'Characteristics of PM (Topic 3)', 'PM model (Topic 4)', 'Environmental education (Topic 5)', 'Bio (Topic 6)', 'Traffic (Topic 7)', 'Asian dust (Topic 8)', 'Indoor PM (Topic 9)', 'Human risk (Topic 10)'. In particular, the proportion of papers on topics 'Government policy and management (Topic 2)', 'PM model (Topic 4)', 'Environmental education (Topic 5)', and 'Bio (Topic 6)' to the toal number of papers increased over time (linear slope > 0). The results of this study provide the new literature review methodology related to particulate matter and the history and insight.