• Journal of dental hygiene science
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• v.24 no.1
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• pp.9-21
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• 2024

Background: The purpose of this study was to utilize text network analysis and topic modeling to identify interconnected relationships among keywords present in patent information related to oral health, and subsequently extract latent topics and visualize them. By examining key keywords and specific subjects, this study sought to comprehend the technological trends in oral health-related innovations. Furthermore, it aims to serve as foundational material, suggesting directions for technological advancement in dentistry and dental hygiene. Methods: The data utilized in this study consisted of information registered over a 20-year period until July 31st, 2023, obtained from the patent information retrieval service, KIPRIS. A total of 6,865 patent titles related to keywords, such as "dentistry," "teeth," and "oral health," were collected through the searches. The research tools included a custom-designed program coded specifically for the research objectives based on Python 3.10. This program was used for keyword frequency analysis, semantic network analysis, and implementation of Latent Dirichlet Allocation for topic modeling. Results: Upon analyzing the centrality of connections among the top 50 frequently occurring words, "method," "tooth," and "manufacturing" displayed the highest centrality, while "active ingredient" had the lowest. Regarding topic modeling outcomes, the "implant" topic constituted the largest share at 22.0%, while topics concerning "devices and materials for oral health" and "toothbrushes and oral care" exhibited the lowest proportions at 5.5% each. Conclusion: Technologies concerning methods and implants are continually being researched in patents related to oral health, while there is comparatively less technological development in devices and materials for oral health. This study is expected to be a valuable resource for uncovering potential themes from a large volume of patent titles and suggesting research directions.

• Knowledge Management Research
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• v.16 no.4
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• pp.69-87
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• 2015

Given the huge number of data produced by its users, SNS is a great source of customer insights. Since viral trends in SNS reflect customers' direct feedback, companies can draw out highly meaningful business insights when such data is effectively analyzed and managed. However, while the importance of understanding SNS big data keeps growing, the methods for analyzing atypical data such as SNS postings for business insights over product has not been well studied. This study aims to demonstrate the way to exploit topic modeling method to support marketing strategy generation and therefore leverage business process. First, we conducted topic modeling analysis for twitter data of Apple and Samsung smartphones. Then we comparatively examined the analysis results to draw meaningful market insights about each smartphone product. Finally, we draw out a strategic marketing recommendation for each smartphone brand based on the findings.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.73-82
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• 2015

Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.130-145
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• 2022

Games are widely used in many fields, but not all games are successful. Then what makes games successful? The question gave us the motivation of this paper, which is to identify critical factors for successful games with topic modeling technique. It is supposed that game reviews written by experts sit on abundant insights and topics of how games succeed. To excavate these insights and topics, latent Dirichlet allocation, a topic modeling analysis technique, was used. This statistical approach provided words that implicate topics behind them. Fifty topics were inferred based on these words, and these topics were categorized by stimulation-response-desiregoal (SRDG) model, which makes a streamlined flow of how players engage in video games. This approach can provide game designers with critical factors for successful games. Furthermore, from this research result, we are going to develop a model for immersive game experiences to explain why some games are more addictive than others and how successful gamification works.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.724-739
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• 2023

Located in the sea area between South Korea, North Korea, and China, the Yellow Sea plays an important role from a geopolitical perspective, and recently, as the use of marine space in the Yellow Sea is expanding, its social and economic values have been increasing further. In addition, owing to rapid climate changes, the need for joint response and cooperation between Korea and China is increasing in various fields, including changes in the marine environment and marine ecosystem and generation and movement of air pollutants. Accordingly, in this study, core topics were derived from research papers with the Yellow Sea as a keyword, and research trends to date were explored through author network analysis. As a specific research method, research papers related to the Yellow Sea published between 1984 and 2021 were extracted from the Web of Science database and were classified into four periods to derive core topics using topic modeling, a type of text mining. Furthermore, the influences of major research communities, researchers, and research institutes in the appropriate fields were identified through analyzing the author network, and their implications were presented. The analysis results indicated that the core topics of research papers on the Yellow Sea had changed over time, and differences existed in the influence (centrality) of key researchers. Finally, based on the results of this study, this study aims to identify research trends related to the Yellow Sea, major researchers, and research institutes and contribute to research cooperation between Korea and China regarding the Yellow Sea in the future.

• The Journal of Industrial Distribution & Business
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• v.10 no.8
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• pp.17-24
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• 2019

Purpose - The purpose of this study is to find out research directions for distribution and fusion and complex field to many domestic and foreign researchers carrying out related academic research by confirming research trends in the Journal of Distribution Science (JDS). Research Design, Data, and Methodology - To do this, I used keywords from a total of 904 papers published in the JDS excluding 19 papers that were not presented with keywords among 923. The analysis utilized word clouding, topic modeling, and weighted frequency analysis using the R program. Results - As a result of word clouding analysis, customer satisfaction was the most utilized keyword. Topic modeling results were divided into ten topics such as distribution channels, communication, supply chain, brand, business, customer, comparative study, performance, KODISA journal, and trade. It is confirmed that only the service quality part is increased in the weighted frequency analysis result of applying to the year group. Conclusion - The results of this study confirm that the JDS has developed into various convergence and integration researches from the past studies limited to the field of distribution. However, JDS's identity is based on distribution. Therefore, it is also necessary to establish identity continuously through special editions of fields related to distribution.

• Convergence Security Journal
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• v.22 no.5
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• pp.61-67
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• 2022

The purpose of this study is to analyze research trends in artificial intelligence. For a three-dimensional analysis, an attempt was made to objectively compare and present the difference between the research direction of artificial intelligence in social science and engineering. For the research method, topic modeling was used among the big data analysis methodologies, and 1000 English papers searched with the keyword artificial intelligence (AI) in the academic research information system were used for the analysis data. As a result of the analysis, in the field of social science, it was possible to identify groups formed around the keywords of 'human', 'impact', and 'future' for artificial intelligence, and in the field of engineering, 'artificial intelligence-based technology development', 'system', 'Groups such as 'Risk-Security' were formed.

• Nuclear Engineering and Technology
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• v.44 no.8
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• pp.855-870
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• 2012

The paper deals with three main topics: a) the definition of System Thermal-Hydraulics (SYS TH), b) a historical outline for SYS TH and, c) the description of elements for reflection when planning research projects or improvement activities, this last topic being the main reason for the paper. Distinctions between basic thermal-hydraulics and computational Fluid-Dynamics (CFD) on the one side and SYS TH on the other side are considered under the first topic; stakeholders in the technology are identified. The proposal of Interim Acceptance Criteria for Emergency Core Cooling Systems in 1971 by US NRC (AEC at the time) is recognized as the starting date or the triggering event for SYS TH (second topic). The complex codes and the main experimental programs (list provided in the paper) constitute the pillars for SYS TH. Caution or warning statements are introduced in advance when discussing the third topic: a single person (or a researcher) has little to no possibility, or capability, of streamlining the forthcoming investments or to propose a roadmap for future activities. Nevertheless, the ambitious attempt to foresee developments in this area has been pursued without constraints connected with the availability of funds and with industrial benefits or interests. Demonstrating the acceptability of current SYS TH limitations and training in the application of those codes are mentioned as the main challenges for forthcoming research activities.

• Knowledge Management Research
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• v.20 no.3
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• pp.139-154
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• 2019

With the increasing use of smartphone apps, many apps are coming out in various fields. In order to analyze the current status and trends of apps in a specific field, it is necessary to establish a classification scheme. Various schemes considering users' behavior and characteristics of apps have been proposed, but there is a problem in that many apps are released and a fixed classification scheme must be updated according to the passage of time. Although it is necessary to consider many aspects in establishing classification scheme, it is possible to grasp the trend of the app through the proposal of a classification scheme according to the characteristic of the app. This research proposes a method of establishing an app classification scheme through the description of the app written by the app developers. For this purpose, we collected explanations about apps in the tourism field and identified major categories through topic modeling. Using only the apps corresponding to the topic, we construct a network of words contained in the explanatory text and identify subcategories based on the networks of words. Six topics were selected, and Clauset Newman Moore algorithm was applied to each topic to identify subcategories. Four or five subcategories were identified for each topic.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.415-423
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• 2017

This study explored research trends by investigating articles published in the Journal of Korean Society of Fashion Design from 2001 through 2015. English key words and abstracts were analyzed using text mining and topic modeling techniques. The findings are as followings. By the text mining technique, 183 core terms, appeared more than 30 times, were derived from 7137 words used in total 338 articles' key words and abstracts. 'Fashion' and 'design' showed the highest frequency rate. After that, the well-received topic modeling technique, LDA, was applied to the collected data sets. Several distinct sub-research domains strongly tied with the previous fashion design field, except for topics such as fashion brand marketing and digital technology, were extracted. It was observed that there are the growing and declining trends in the research topics. Based on findings, implication, limitation, and future research questions were presented.