Mapping the COVID-19 Issues from an Urban Perspective in South Korea - Text Mining Analysis Focused on Newspaper Articles

This paper reviews the development of South Korea’s national research and development (R&D) in graphene technology, focusing on projects that have been classified as “green” technology. A total of 826 projects (USD 210 billion) from 2010 to 2019 were collected from the National Science and Technology Information Service (NTIS), which is full-cycle national R&D project management system in South Korea. Then we analyzed its R&D trend by conducting diverse text mining methods including frequency analysis, association rule mining, and topic modeling. The analysis suggests that the number of graphene green technology (GT) R&D projects and the research expenses will show a rising curve again in the incumbent government along with the implementation of the Korean New Deal policy, which integrates the Green New Deal and the Digital New Deal.

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Mapping Environmental Conflicts Using Spatial Text Mining

Land ◽

10.3390/land9090287 ◽

2020 ◽

Vol 9 (9) ◽

pp. 287 ◽

Cited By ~ 2

Author(s):

Jae-hyuck Lee ◽

Do-kyun Kim

Keyword(s):

Text Mining ◽

South Korea ◽

Large Scale ◽

Local Environment ◽

Environmental Conflict ◽

Current Status ◽

Environmental Conflicts ◽

Mining Technique ◽

Management Policies ◽

Definition Of

Mapping the characteristics and extent of environmental conflicts related to land use is important for developing regionally specific policies. However, because it is only possible to verify the frequency of conflicts on a specific predetermined subject, it is difficult to determine the various reasons for conflicts in a region. Therefore, this study mapped the current status of regional environmental conflicts in South Korea using a spatial text mining technique, then proposed relevant management policies. The results were obtained by analyzing environmental conflict data extracted from the online agendas of regional environmental organizations. Air quality-related conflicts in South Korea are concentrated in western municipalities; development-related conflicts are concentrated in the southern region of Jeju Island; and intensive safety-related conflicts occur in metropolitan areas, particularly Ulsan. Thus, the type of conflict is determined by the local environment, in accordance with the definition of environmental conflict, and the distribution is determined by the location of the stakeholder population. This study reveals the issues and locations related to local environmental conflict that require further attention, and proposes more wide-ranging methods for managing the links between conflicts by mapping environmental conflicts on a large scale rather than on an individual basis.

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The Method to Analyze Freely Described Data from Questionnaires

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2009.p0268 ◽

2009 ◽

Vol 13 (3) ◽

pp. 268-274 ◽

Cited By ~ 3

Author(s):

Masaomi Kimura ◽

Keyword(s):

Text Mining ◽

Call Center ◽

Clustering Algorithms ◽

Web Pages ◽

Research Papers ◽

Text Data ◽

Textual Data ◽

The Way ◽

Newspaper Articles

Text mining has been growing; mainly due to the need to extract useful information from vast amounts of textual data. Our target here is text data, a collection of freely described data from questionnaires. Unlike research papers, newspaper articles, call-center logs and web pages, which are usually the targets of text mining analysis, the freely described data contained in the questionnaire responses have specific characteristics, including a small number of short sentences forming individual pieces of data, while the wide variety of content precludes the applications of clustering algorithms used to classify the same. In this paper, we suggest the way to extract the opinions which are delivered by multiple respondents, based on the modification relationships included in each sentence in the freely described data. Certain applications of our method are also presented after the introduction of our approach.

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