• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.171-187
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• 2019

The study aims to analyze the posts of depression-related Facebook groups to understand major topics discussed by group users. Specifically, the purpose of the study is to identify the topics and keywords of the posts to understand what users discuss about depression. Depression is a mental disorder that is somewhat sensitive in the online community, which is characterized by accessibility, openness and anonymity. The researchers have implemented a natural language-based data analysis framework that includes components ranging from Facebook data collection to the automated extraction of topics. Using the framework, we collected and analyzed 885 posts created in the past one year from the largest Facebook depression group. To derive more complete and accurate topics, we combined both automated and manual (e.g., stop words removal, topic size determination) methods. Results indicate that users discuss a variety of topics including depression in general, human relations, mood and feeling, depression symptoms, suicide, medical references, family and etc.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.308-321
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• 2021

The purpose of this study was to analyze domestic research trends over the past 20 years and future direction of forest ecology using text mining. A total of 1,015 academic papers and keywords data related to forest ecology were collected by the "Research and Information Service Section" and analyzed using big data analysis programs, such as Textom and UCINET. From the results of word frequency and N-gram analyses, we found domestic studies on forest ecology rapidly increased since 2011. The most common research topic was "species diversity" over the past 20 years and "climate change" became a major topic since 2011. Based on CONCOR analysis, study subjects were grouped intoeight categories, such as "species diversity," "environmental policy," "climate change," "management," "plant taxonomy," "habitat suitability index," "vascular plants," and "recreation and welfare." Consequently, species diversity and climate change will remain important topics in the future and diversifying and expanding domestic research topics following global research trendsis necessary.

• Journal of Information Science Theory and Practice
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• v.9 no.3
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• pp.76-106
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• 2021

The study analyzed bibliometric data of papers published in Korea Citation Index (KCI) and Web of Science (WoS) journals from 2002 to 2021. After examining size differences of KCI and WoS domains in the number of authors, institutions, and journals to put publication and citations counts in perspective, the study investigated co-authorship patterns over time to compare collaboration trends of Korean and international scholars and analyzed the data at author, institution, and journal levels to explore how the influences of authors, institutions, and journals on research output differ across domains. The study also conducted frequency-based analysis of keywords to identify key topics and visualized keyword clusters to examine topic trends. The result showed Korean LIS authors to be twice as productive as international authors but much less impactful and Korean institutions to be at comparable levels of productivity and impact in contrast to much of productivity and impact concentrated in top international institutions. Citations to journals exhibited initially increasing pattern followed by a decreasing trend though WoS journals showed far more variance than KCI journals. Co-authorship trends were much more pronounced among international publication, where larger collaboration groups suggested multi-disciplinary and complex nature of international LIS research. Keyword analysis found continuing diversification of topics in international research compared to relatively static topic trend in Korea. Keyword visualization showed WoS keyword clusters to be much denser and diverse than KCI clusters. In addition, key keyword clusters of WoS were quite different from each other unlike KCI clusters which were similar.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.195-203
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• 2021

Recently, big data, a major technology in the IT field, has been expanding into various industrial sectors and research on how to utilize it is actively underway. In most Internet industries, user reviews help users make decisions about purchasing products. However, the process of screening positive, negative and helpful reviews from vast product reviews requires a lot of time in determining product purchases. Therefore, this paper designs and implements a system that analyzes and aggregates keywords using LDA, a big data analysis technology, to provide meaningful information to users. For the extraction of document topics, in this study, the domestic book industry is crawling data into domains, and big data analysis is conducted. This helps buyers by providing comprehensive information on products based on user review topics and appraisal words, and furthermore, the product's outlook can be identified through the review status analysis.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.43-50
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• 2021

This paper aims to identify trends of domestic industry-academic cooperation through comparative analysis of domestic and overseas academic articles published over the past 10 years (2011-2021). To this end, keyword network analysis and topic modeling analysis were performed to identify the characteristics of the entire articles collected. As results, it turned out that domestic articles included school, employment, education, patent, and professor as a major keyword while for overseas articles, project, policy, innovation, and company were the main topics, and related keywords were found to be influential. These results suggest that domestic industry-academic cooperation would have been designed and led by universities focusing on education for employment, and need to be carried out more actively in the areas of 'research' and 'technology transfer with the government's related policies and support on establishing two-way relationships that can benefit both schools and participating companies.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.430-440
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• 2021

We analyzed research trends of 288 Korean academic dissertations and articles regarding platform work, using topic modeling and keyword network analysis method. Research disciplines of many studies were laws, business administration, and economics fields. Thigh frequent themes were platform labor protection measures and direct or indirect effects of the sharing economy. The main keywords were digital, value, industry, and labor in terms of infrastructure and structural change. Besides, the main topics were the protection of platform workers, the values of sharing services, digital paradigm, and platform regulations. Based on the results of the analysis, we derived four implications and suggestions such as researching structural frames in macroscopic contexts, generalizing case analysis, and technology supplementation by applying average and quantitative analysis methods, researching individual competency development to realize the essential symbiotic value of sustainability, and developing customized vocational education and training programs.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.111-122
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• 2021

This study analyzed user reviews of Dongbaekjeon and Incheoneum app, which are representative local currencies in Korea, to identify the positive/negative factors of local currency users, and established a marketing strategy based on this. App user reviews were classified into positive and negative based on the star rating, and word cloud, topic modeling, and social network analysis were performed, respectively. As a result, in the negative reviews of Dongbaekjeon and Incheoneum, dissatisfaction with app use and card issuance appeared in common. In positive reviews, keywords such as 'local economy' and 'small business owners' along with satisfaction with 'cashback' appeared. It means that local currency users perceived that their consumption support local economy, and they felt satisfaction in using local currency. Based on the satisfaction/dissatisfaction factors identified as a result of the analysis of this study, we identified what needs to be improved and to be strengthened, and appropriate marketing strategies were established. The text mining method used in this study and research results can provide meaningful information about local currencies to public officials and marketers in charge of local currencies.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.289-300
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• 2021

SNS data was collected and analyzed by topic modeling techniques to examine recent trends in information education. By deriving keywords and topics for SW education and AI education, we not only attempted to discover insights ahead of the next revised curriculum but also suggested directions. According to the SNS data analysis, the contents of human resource development for software and the instructional method in schools are indicated as a high requirement. Meanwhile, SW education should be conducted through a separate curriculum from elementary school, and this was consistent with the opinion that it is necessary to be organized as a required subject. There was an opinion to support the schools since AI education is newly introduced in next revised national curriculum. The trends in SW education and AI education which are observed through SNS data analysis could be concluded to conduct the substantial operation of information education and curriculum organization.

• The Journal of Information Systems
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• v.31 no.1
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• pp.175-195
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• 2022

Purpose The purpose of this study is to examine news articles and analyze trends and key agendas related to artificial intelligence(AI). In particular, this study tried to compare the reporting behaviors of Korea and the United States, which is considered to be a leader in the field of AI. Design/methodology/approach This study analyzed news articles using a big data method. Specifically, main agendas of the two countries were derived and compared through the keyword frequency analysis, topic modeling, and language network analysis. Findings As a result of the keyword analysis, the introduction of AI and related services were reported importantly in Korea. In the US, the war of hegemony led by giant IT companies were widely covered in the media. The main topics in Korean media were 'Strategy in the 4th Industrial Revolution Era', 'Building a Digital Platform', 'Cultivating Future human resources', 'Building AI applications', 'Introduction of Chatbot Services', 'Launching AI Speaker', and 'Alphago Match'. The main topics of US media coverage were 'The Bright and Dark Sides of Future Technology', 'The War of Technology Hegemony', 'The Future of Mobility', 'AI and Daily Life', 'Social Media and Fake News', and 'The Emergence of Robots and the Future of Jobs'. The keywords with high centrality in Korea were 'release', 'service', 'base', 'robot', 'era', and 'Baduk or Go'. In the US, they were 'Google', 'Amazon', 'Facebook', 'China', 'Car', and 'Robot'.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.418-431
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• 2022

Purpose: We propose a new scheme for predicting promise technology and it improves the conventional scheme that misses important lists of patent because of insufficient search formula, and cannot reflect new trend of technology due to the unreleased period of patents. Method: In this paper, we propose a new search formula exploiting TF and TF-IDF with R programming as well as related keywords, and LDA topic modeling scheme is used for analyzing recently published papers in Satellite·Terrestrial Information Convergence Disaster Response. Result: By comparing both schemes with commercial DB, the proposed scheme can find more important patents, and can reflect new trend of technology, compared to the conventional scheme. Conclusion: The proposed scheme can be used to predict promise technologies in the field of Satellite·Terrestrial Information Convergence Disaster Response.