• Journal of Korean Biological Nursing Science
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• 제23권3호
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• pp.170-179
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• 2021

Purpose: The aim of this study was to identify core keywords and topic groups in the 'Diabetes mellitus and mobile applications' field of research for better understanding research trends in the past 20 years. Methods: This study was a text-mining and topic modeling study including four steps such as 'collecting abstracts', 'extracting and cleaning semantic morphemes', 'building a co-occurrence matrix', and 'analyzing network features and clustering topic groups'. Results: A total of 789 papers published between 2002 and 2021 were found in databases (Springer). Among them, 435 words were extracted from 118 articles selected according to the conditions: 'analyzed by text network analysis and topic modeling'. The core keywords were 'self-management', 'intervention', 'health', 'support', 'technique' and 'system'. Through the topic modeling analysis, four themes were derived: 'intervention', 'blood glucose level control', 'self-management' and 'mobile health'. The main topic of this study was 'self-management'. Conclusion: While more recent work has investigated mobile applications, the highest feature was related to self-management in the diabetes care and prevention. Nursing interventions utilizing mobile application are expected to not only effective and powerful glycemic control and self-management tools, but can be also used for patient-driven lifestyle modification.

• 한국정보시스템학회지:정보시스템연구
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• 제31권4호
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• pp.161-187
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• 2022

Purpose This study analyzed the topic of game review contents and how the characteristics of game reviews affect the reviews helpfulness. In addition, this study explore the content of game reviews according to the game's sales strategy such as early access strategy and releasing without early access. Design/methodology/approach We collected a list of 3,572 action genre games released in 2020. 58,336 online reviews were collected by random sampling 50 reviews in each games, and topic modeling was performed on those reviews. We dynamized the results of topic modeling and analyzed the effect on review helpfulness with multiple regression analysis. Findings The results of analysis indicate that the longer the review is or the shorter the time it is written, the more helpful the review is. In addition the topic with positive and negative review has a significant effect on the review helpfulness. As a result of exploratory analysis, games from early access had relatively fewer reviews of story-related topics than games that were released without early access. These findings can present direct guidelines for collecting specific opinions from customers in the game industry when releasing games.

• 한국정보시스템학회지:정보시스템연구
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• 제30권1호
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• pp.45-64
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• 2021

Purpose The traditional topic modeling technique makes it difficult to distinguish the semantic of topics because the key words assigned to each topic would be also assigned to other topics. This problem could become severe when the number of online reviews are small. In this paper, the extended model of topic modeling technique that can be used for analyzing a small amount of online reviews is proposed. Design/methodology/approach The extended model of being proposed in this paper is a form that combines the traditional topic modeling technique and the Word2Vec technique. The extended model only allocates main words to the extracted topics, but also generates discriminatory words between topics. In particular, Word2vec technique is applied in the process of extracting related words semantically for each discriminatory word. In the extended model, main words and discriminatory words with similar words semantically are used in the process of semantic classification and naming of extracted topics, so that the semantic classification and naming of topics can be more clearly performed. For case study, online reviews related with Udo in Tripadvisor web site were analyzed by applying the traditional topic modeling and the proposed extension model. In the process of semantic classification and naming of the extracted topics, the traditional topic modeling technique and the extended model were compared. Findings Since the extended model is a concept that utilizes additional information in the existing topic modeling information, it can be confirmed that it is more effective than the existing topic modeling in semantic division between topics and the process of assigning topic names.

• 한국산학기술학회논문지
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• 제19권10호
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• pp.176-182
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• 2018

본 연구는 건설 안전사고에 대한 트랜드 분석을 위해 LDA(Latent Dirichlet Allocation) 기반의 토픽모델링(Topic Modeling)을 제시하여 분석하고자 한다. 특히, 건설산업의 안전사고를 예방하기 위해 제시되고 있는 기존의 다양한 정형데이터 분석에서 벗어난 비정형 데이터 분석 기반의 토픽 모델링을 통해 건설 안전사고 주요 핵심 키워드의 흐름에 대해 파악이 가능하다. 본 방법론을 적용하기 위해 540개의 건설 안전사고 관련 뉴스데이터를 수집하였다. 이를 기반으로, 10가지 토픽과 각 토픽 내의 10가지 키워드를 통해 주요 이슈를 도출하였고 각 토픽에 대한 2017년 1월부터 2018년 2월까지의 뉴스 데이터를 월별 시계열 분석을 통해 향후 토픽에 관한 이슈를 예측한다. 본 연구를 바탕으로 향후 건설 안전사고의 다양한 이슈를 선제적으로 예측하고 이를 기반으로 건설 안전사고 정책과 연구에 좋은 방향을 제시할 것으로 판단한다.

• 한국간호교육학회지
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• 제27권2호
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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.

• Journal of Information Science Theory and Practice
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• 제6권4호
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• pp.39-53
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• 2018

While the body of research on metadata has grown substantially, there has been a lack of systematic analysis of the field of metadata. In this study, we attempt to fill this gap by examining metadata literature spanning the past 20 years. With the combination of a text mining technique, topic modeling, and network analysis, we analyzed 2,713 scholarly papers on metadata published between 1995 and 2014 and identified main topics and trends in metadata research. As the result of topic modeling, 20 topics were discovered and, among those, the most prominent topics were reviewed in detail. In addition, the changes over time in the topic composition, in terms of both the relative topic proportions and the structure of topic networks, were traced to find past and emerging trends in research. The results show that a number of core themes in metadata research have been established over the past decades and the field has advanced, embracing and responding to the dynamic changes in information environments as well as new developments in the professional field.

• 지식경영연구
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• 제16권4호
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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.

• 한국응급구조학회지
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• 제26권2호
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• pp.19-35
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• 2022

Purpose: This study aimed to identify research topics in the emergency medical technician (EMT) field and examine research trends. Methods: In this study, 261 research papers published between January 2000 and May 2022 were collected, and EMT research topics and trends were analyzed using topic modeling techniques. This study used a text mining technique and was conducted using data collection flow, keyword preprocessing, and analysis. Keyword preprocessing and data analysis were done with the RStudio Version 4.0.0 program. Results: Keywords were derived through topic modeling analysis, and eight topics were ultimately identified: patient treatment, various roles, the performance of duties, cardiopulmonary resuscitation, triage systems, job stress, disaster management, and education programs. Conclusion: Based on the research results, it is believed that a study on the development and application of education programs that can successfully increase the emergency care capabilities of EMTs is needed.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 봄 학술논문 발표대회
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• pp.31-32
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• 2022

The construction industry has more safety accidents than other industries. Although there have been more attempts to reduce safety hazards in the industry such as the enforcement of the "Serious Accidents Punishment Act (SAPA)", construction accident has not been reduced enough. In this study, analysis of safety risk factors has been made through Latent Dirichlet Allocation (LDA)-based topic modeling. Risk analysis in construction site would be improved with natural language processing and topic modeling.

• 산경연구논집
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• 제13권9호
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• pp.37-50
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• 2022

Purpose: The purpose of this study is to explore and compare research trends in Korea and overseas academic papers on social media marketing, and to present new academic perspectives for the future direction in Korea. Research design, data and methodology: We used English abstract of research paper (Korea's: 1,349, overseas': 5,036) for word frequency analysis, topic modeling, and trend analysis for each topic. Results: The results of word frequency and co-occurrence frequency analysis showed that Korea researches focused on the experiential values of users, and overseas researches focused on platforms and content. Next, 13 topics and 12 topics for Korea and overseas researches were derived from topic modeling. And, trend analysis showed that Korean studies were different from overseas in applying marketing methods to specific industries and they were interested in the short-term performance of social media marketing. Conclusions: We found that the long-term strategies of social media marketing and academic interest in the overall industry will necessary in the future researches. Also, data mining techniques will necessary to generate more general results by quantifying various phenomena in reality. Finally, we expected that continuous and various academic approaches for volatile social media is effective to derive practical implications.