• 한국식생활문화학회지
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• 제35권1호
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• pp.65-78
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• 2020

The objective of this study was to review and evaluate the growing subject of food tourism research, and thus identify the trend of food tourism research. Using a Text mining technique, this paper discovered the trends of the literature on food tourism that was published from 2004 to 2018. The study reviewed 201 articles that include the words 'food' and 'tourism' in their abstracts in the KCI database. The Wordscloud analysis results presented that the research subjects were predominantly 'Festival', 'Region', 'Culture', 'Tourist', but there was a slight difference in frequency according to the time period. Based on the main path analysis, we extracted the meaningful paths between the cited references published domestically, resulting in a total of 12 networks from 2004 to 2018. The Text network analysis indicated that the words with high centrality showed similarities and differences in the food tourism literature according to the time period, displaying them in a sociogram, a visualization tool. This study has implications that it offers a new perspective of comprehending the overall flow of relevant research.

• 한국안전학회지
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• 제37권3호
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• pp.9-15
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• 2022

The analysis of the causes of accidents in workplaces where machines and tools are used is essential to improve the effectiveness and efficiency of safety prevention policies in places of employment in Korea. The causes of workplace accidents are not fully understood mainly due to difficulties in analyzing available descriptive information. This study focuses on the automated accident cause analysis in workplaces based on the accident abstracts found in industrial accident reports written in an unstructured descriptive format. The method proposed in this paper is based on text data mining and uses the keyword search function of Excel software to automate the analysis. The analysis results indicate that the primary reason for the frequency of accidents is related to technical aspects at a stage in which dangerous situations occur in the workplace. Accidents due to managerial causes are typically observed when danger exists in the workplace; however, managerial actions play a more important role in reducing accident severity. A small company tends to use unsafe machines and devices, leading to further accidents due to technical causes, whereas managerial causes are more conspicuous as the company grows. To preclude the occurrence of accidents due to inadequate knowledge, the implementation of safety management and the provision of safety education to elderly workers at the early stage of their employment are particularly important for small companies with less than 100 workers.

• International journal of advanced smart convergence
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• 제12권1호
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• pp.164-172
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• 2023

In this study, we identify the recognition of 'The First Slam Dunk', which is gaining popularity as a sports-based cartoon through big data analysis of social media channels, and provide basic data for the development and development of various contents in the sports industry. Social media channels collected detailed social big data from news provided on Naver and Google sites. Data were collected from January 1, 2023 to February 15, 2023, referring to the release date of 'The First Slam Dunk' in Korea. The collected data were 2,106 Naver news data, and 1,019 Google news data were collected. TF and TF-IDF were analyzed through text mining for these data. Through this, semantic network analysis was conducted for 60 keywords. Big data analysis programs such as Textom and UCINET were used for social big data analysis, and NetDraw was used for visualization. As a result of the study, the keyword with the high frequency in relation to the subject in consideration of TF and TF-IDF appeared 4,079 times as 'The First Slam Dunk' was the keyword with the high frequency among the frequent keywords. Next are 'Slam Dunk', 'Movie', 'Premiere', 'Animation', 'Audience', and 'Box-Office'. Based on these results, 60 high-frequency appearing keywords were extracted. After that, semantic metrics and centrality analysis were conducted. Finally, a total of 6 clusters(competing movie, cartoon, passion, premiere, attention, Box-Office) were formed through CONCOR analysis. Based on this analysis of the semantic network of 'The First Slam Dunk', basic data on the development plan of sports content were provided.

• 한국조리학회지
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• 제24권2호
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• pp.34-43
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• 2018

The objective of this study was to acquire a general and text-based awareness and recognition of cruise food hygiene through big data analytics. For the purpose, this study collected data with conducting the keyword "food hygiene, cruise" on the web pages and news on Google, during October 1st, 2015 to October 1st, 2017 (two years). The data collection was processed by SCTM which is a data collecting and processing program and eventually, 899 kb, approximately 20,000 words were collected. For the data analysis, UCINET 6.0 packaged with visualization tool-Netdraw was utilized. As a result of the data analysis, the words such as jobs, news, showed the high frequency while the results of centrality (Freeman's degree centrality and Eigenvector centrality) and proximity indicated the distinct rank with the frequency. Meanwhile, as for the result of CONCOR analysis, 4 segmentations were created as "food hygiene group", "person group", "location related group" and "brand group". The diagnosis of this study for the food hygiene in cruise industry through big data is expected to provide instrumental implications both for academia research and empirical application.

• 대한원격탐사학회지
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• 제33권2호
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• pp.189-199
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• 2017

본 연구는 텍스트 마이닝 기법을 활용하여 환경 분야에서 ICT의 활용 연구동향을 정량적으로 분석하였다. 이를 위해 환경 분야 키워드 38개, ICT 관련 키워드 16개를 바탕으로 국가과학기술정보센터(NDSL)에서 최근 20년(1996년-2015년)의 논문 359편을 수집하였다. 해당 논문을 대상으로 환경 분야 및 ICT 관련 자연어를 처리하여 말뭉치(Corpus)단위로 분류체계를 재구성하였다. 전술된 분류체계의 키워드를 바탕으로 텍스트 마이닝 분석 기법인 빈도 분석, 키워드 분석, 키워드 간 연관규칙을 확인하였다. 그 결과 '환경 일반' 및 '기후' 분야의 키워드 출현 빈도가 전체의 77 %, ICT는 '공공융합서비스' 및 '산업융합서비스'가 약 30 %의 비율을 차지하였다. 시계열 분석을 통해 환경 분야에서의 ICT 활용 연구는 최근 5년(2011년-2015년)사이에 급증하여 과거(1996년-2010년)과 비교하여 약 2배 이상 관련 연구가 증가된 것으로 나타났다. 키워드 간 연관 규칙을 생성하여 환경 분야를 기준으로 나타내었을 때, '환경 일반'은 16개, '기후'는 '14'개의 ICT 기반 기술을 주로 활용하고 있는 것으로 확인하였다.

• 한국IT서비스학회지
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• 제14권1호
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• pp.237-249
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• 2015

The volume of unstructured text data generated by various social media has been increasing rapidly; therefore, use of text mining to support decision making has also been increasing. Especially, issue Clustering-determining a new relation with various issues through clustering-has gained attention from many researchers. However, traditional issue clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be discovered using traditional issue clustering methods, even if those issues are strongly related in other perspectives. Therefore, issue clustering that fits each of criteria needs to be performed by the perspective of analysis and the purpose of use. In this study, a multi-dimensional issue clustering is proposed to overcome the limitation of traditional issue clustering. We assert, specifically in this study, that issue clustering should be performed for a particular purpose. We analyze the results of applying our methodology to two specific perspectives on issue clustering, (i) consumers' interests, and (ii) related R&D terms.

• 공학교육연구
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• 제25권5호
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• pp.85-93
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• 2022

Capstone Design, a project-based learning technique, is the most important curriculum that clarifying major knowledge and cultivating the ability to apply through the process of solving problems in the industrial field centered on the student project team. Accordingly, various and extensive studies are being conducted for the successful implementation of capstone design courses. Unlike previous studies, this study aimed to quantitatively analyze the opinions that recorded the experiences and feelings of students who performed capstone design, and used text mining methodologies such as frequency analysis, correlation analysis, topic modeling, and sentiment analysis. As a result of examining the overall opinions of the latter period through frequency analysis and correlation analysis, there was a difference between the languages used by the students in the opinions according to gender and project results. Through topic modeling analysis, 'topic selection' and 'the relationship between team members' showed an increase in occupancy or high occupancy, and topics such as 'presentation', 'leadership', and 'feeling what they felt' showed a tendency to decreasing occupancy. Lastly, sentiment analysis has found that female students showed more neutral emotions than male students, and the passed group showed more negative emotions than the non-passed group and less neutral emotions. Based on these findings, students' practical recognition of the curriculum was considered and implications for the improvement of capstone design were presented.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1526-1533
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• 2018

This study aims to provide implications for mobile foreign language vocabulary learning app by analyzing the effects of mobile vocabulary learning app based on character animation. For this purpose, we applied the learning application designed with character animation and text, and the application designed with text only to two groups of learners, and analyzed the effect. As a result, we found that application designed with character animation and text was useful in recognition frequency and duration concerning learning. Regarding learning outcomes, we found that it is useful not only in memory but also in learning interest and motivation. This study provides implications for learning method and design development of mobile-based foreign language vocabulary learning application which actively using recently.

• 컴퓨터교육학회논문지
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• 제22권3호
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• pp.67-78
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• 2019

본 연구의 목적은 Web of Science(WoS)에서 제공하는 학술 빅데이터를 활용하여 정보윤리 이슈를 탐색하고 향후 정보과 정보윤리 교육을 위한 시사점을 제공하는 것에 있다. 이를 위해 WoS에서 제공하는 학술논문 중 정보윤리와 관련해 출판된 318편의 논문을 텍스트 마이닝 하였다. 구체적으로는 R을 활용해 주요키워드에 대한 빈도 분석(TF, DF, TF-IDF), 토픽 모델링 기반의 정보윤리 이슈 분석, 그리고 각 이슈에 대한 연도별 출연 빈도를 분석하여 정보윤리 연구의 경향성을 탐색하였다. 주요 결과를 살펴보면 다음과 같다. 첫째, TF-IDF를 통해 'digital', 'student', 'software', 'privacy' 등의 단어가 주요 키워드임을 확인하였다. 둘째, 토픽 모델링 분석 결과, 'Professional value', 'Cyber-bullying', 'AI and Social Impact' 등을 포함한 총 8개 이슈로 분석되었고, 그 중, 'Professional value'와 'Cyber-bullying' 이슈가 상대적으로 높은 비율을 차지하고 있었다. 본 연구는 이러한 분석 결과를 기초로 우리나라 정보윤리 교육을 시사점을 논의하였다.

• 한국정보시스템학회지:정보시스템연구
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• 제28권2호
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• pp.153-179
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• 2019

Purpose This study collects the issues about work-life balance in Korea and United States and suggests the specific plans for work-life balance by the comparison and analysis. The objective of this study is to contribute to the improvement of people's life quality by understanding the concept of work-life balance that has become the issue recently and offering the detailed plans to be considered in respect of individual, corporate and governmental level for society of work-life balance. Design/methodology/approach This study collects work-life balance related issues through recruit sites in Korea and United States, compares and analyzes the collected data from the results of three text mining techniques such as LDA topic modeling, term frequency analysis and keyword extraction analysis. Findings According to the text mining results, this study shows that it is important to build corporate culture that support work-life balance in free organizational atmosphere especially in Korea. It also appears that there are the differences against whether work-life balance can be achieved and recognition and satisfaction about work-life balance along type of company or sort of working. In case of United States, it shows that it is important for them to work more efficiently by raising teamwork level among team members who work together as well as the role of the leaders who lead the teams in the organization. It is also significant for the company to provide their employees with the opportunity of education and training that enables them to improve their individual capability or skill. Furthermore, it suggests the roles of individuals, company and government and specific plans based on the analysis of text mining results in both countries.