• Journal of Digital Convergence
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• v.19 no.12
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• pp.29-42
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• 2021

As the low fertility intensifies in Korea, this study investigated fundamental differences between the government's low fertility policy and public perception of it. To this end, we selected four times 'Aging Society and Population Policy' documents and news comments for two weeks immediately after announcement of the third and fourth Policy as analysis targets. Then we conducted word frequency analysis, co-occurrence analysis and CONCOR analysis. As a result of analyses, first, direct childcare support during the first and second periods, and a social structural approach during third and fourth periods were noticeable. Second, it was revealed that both policies and comments aim for the work-family compatibility in 'parenting'. Lastly it was showed public interest in environment of raising children and the critical mind to effectiveness of the policy. This study is meaningful in that it confirmed the public perception using big data analysis, and it will help improve the direction for the future low fertility policy.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.96-107
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• 2023

The objective of this study is to explore future issues that Chinese users, who have the highest mobile payment service usage rate in the world, will be most interested in. For this purpose, after collecting text data from a Chinese SNS site, it classifies major keywords into 4 types of future signals by using Keyword Emergence Map (KEM) and Keyword Issue Map (KIM). Furthermore, to understand the four types of signals in detail, it performs the qualitative analysis on text related to each signal keyword. As a result, it finds that the strong signal, which is rapidly growing in keyword appearance frequency during this research period, includes the keywords related to the daily life of Chinese people, such as buses, subways, and household account books. Additionally, it find that the signal that appears frequently now, but with a low increase rate, includes various services that can replace cash payment, such as hongbao (cash payment) and bank cards. The weak signal and latent signal, which appear less often than other two signals, includes the keywords related to promotion events or changes in service regulations. Its result shows that the mobile payment services greatly have changed user's daily life beyond providing convenience. Furthermore, it shows that, in the Chinese market, in which card payment is not common, the mobile payment services have the great potential to completely replace cash payment.

• Journal of the Korean Society for information Management
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• v.30 no.1
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• pp.285-302
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• 2013

This study identified topic shifts and patterns over time by analyzing an enormous amount of Twitter data whose characteristics are high accessibility and briefness. First, we extracted keywords for a certain product and used them for representing the topic network allows for intuitive understanding of keywords associated with topics by nodes and edges by co-word analysis. We conducted temporal analysis of term co-occurrence as well as topic modeling to examine the results of network analysis. In addition, the results of comparing topic shifts on Twitter with the corresponding retrieval results from newspapers confirm that Twitter makes immediate responses to news media and spreads the negative issues out quickly. Our findings may suggest that companies utilize the proposed technique to identify public's negative opinions as quickly as possible and to apply for the timely decision making and effective responses to their customers.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.431-453
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• 2022

Hate is a collective expression of exclusivity toward others and it is fostered and reproduced through false public perception. This study aims to explore the objects and issues of hate discussed in our society using text mining techniques. To this end, we collected 17,867 news data published from 1990 to 2020 and constructed a co-word network and cluster analysis. In order to derive an explicit co-word network highly related to hate, we carried out sentence split and extracted a total of 52,520 sentences containing the words 'hate', 'prejudice' and 'discrimination' in the preprocessing phase. As a result of analyzing the frequency of words in the collected news data, the subjects that appeared most frequently in relation to hate in our society were women, race, and sexual minorities, and the related issues were related laws and crimes. As a result of cluster analysis based on the co-word network, we found a total of six hate-related clusters. The largest cluster was 'genderphobic', accounting for 41.4% of the total, followed by 'sexual minority hatred' at 28.7%, 'racial hatred' at 15.1%, 'selective hatred' at 8.5%, 'political hatred' accounted for 5.7% and 'environmental hatred' accounted for 0.3%. In the discussion, we comprehensively extracted all specific hate target names from the collected news data, which were not specifically revealed as a result of the cluster analysis.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.5
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• pp.90-102
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• 2022

In recent years, as interest in healing increases, outdoor spaces with the concept of healing have been created. For more professional and in-depth planning and design, the perception and characteristics of outdoor healing places through social media posts were analyzed using NER. Text mining was conducted using 88,155 blog posts, and frequency analysis and clique cohesion analysis were conducted. Six elements were derived through a literature review, and two elements were added to analyze the perception and the characteristics of healing places. As a result, visitors considered place elements, date and time, social elements, and activity elements more important than personnel, psychological elements, plants and color, and form and shape when visiting healing places. The analysis allowed the derivation of perceptions and characteristics of healing places through keywords. From the results of the Clique, keywords, such as places, date and time, and relationship, were clustered, so it was possible to know where, when, what time, and with whom people were visiting places for healing. Through the study, the perception and characteristics of healing places were derived by analyzing large-scale data written by visitors. It was confirmed that specific elements could be used in planning and marketing.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.38 no.2
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• pp.77-94
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• 2020

Gugok poems were an important material for studying the Gugok landscape garden(九曲園林). the landscape feature(景物)that emerges from poetry is an important material that expresses the sense of placefulness and garden consciousness(園林意識). However, many studies are passive about plant materials that can explore the perception of nature or the aesthetic embodiment process. Therefore, this study seeks to discover various symbolic meanings and reveal the context of plants that have appeared in Gugok poems in order to expand the research material of the Gugok culture. To carry out the purpose of the study, I collected a total of 25 related poems from 18 Gugoks and found a total of 20 species of trees. I used 'R-program' to derive the meaning of trees and examined the meaning of trees by intertextuality. According to the study, the 20 species of trees contained symbolic meanings of world of Taoist hermit, pursuit of study, constancy, true pleasure, dignity, honest poverty, reign of peace, nostalgia. Many species focused on the symbolism of the 'world of Taoist hermit' and then on the 'dignity' was the most frequent. A number of species, except for the peach, zelkova and oak, had multiple meanings. Among them, pine trees and lotus had a wide range of symbolic meanings and different meanings depending on the characteristics of the surrounding landscape. While the Gugok culture generally targets natural scenery, Yongsan, Toegye, Deoksan, and Jusan showed the characteristics of strengthening or reproducing symbolic meanings through artificial plating and gardening. In order to overcome the limitations of the peach tree, which symbolizes 'Mureungdowon(武陵桃園)', Gugok poems used maple trees and reeds as alternative species. In accordance with the above context, the trees appearing in the Gugok poems expressed their symbolic meaning differently according to the landscape features, acts and purposes of the Gugoks, rather than sticking to the traditional meaning.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.189-199
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• 2017

Thisstudy quantitatively analyzed the research trendsin the use ofICT ofthe environmental field using the text mining technique. To that end, the study collected 359 papers published in the past two decades(1996-2015)from the National Digital Science Library (NDSL) using 38 environment-related keywords and 16 ICT-related keywords. It processed the natural languages of the environment and ICT fields in the papers and reorganized the classification system into the unit of corpus. It conducted the text mining analysis techniques of frequency analysis, keyword analysis and the association rule analysis of keywords, based on the above-mentioned keywords of the classification system. As a result, the frequency of the keywords of 'general environment' and 'climate' accounted for 77 % of the total proportion and the keywords of 'public convergence service' and 'industrial convergence service' in the ICT field took up approximately 30 % of the total proportion. According to the time series analysis, the researches using ICT in the environmental field rapidly increased over the past 5 years (2011-2015) and the number of such researches more than doubled compared to the past (1996-2010). Based on the environmental field with generated association rules among the keywords, it was identified that the keyword 'general environment' was using 16 ICT-based technologies and 'climate' was using 14 ICT-based technologies.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.29-43
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• 2021

Coinciding with the advent of COVID-19, online education is on the rise both domestically and globally, and has become an absolutely necessary and irreplaceable form of education. It is a very curious question what the perception of people about the suddenly growing form of education is, and how it has changed. This study investigated changes in consumers' perception of online education using big data. To this end, we divided the time into four stages: before COVID-19 (November to December 2019), after the triggering of COVID-19 (January to February 2020), right after the online classes started (March to April 2020), after experiencing some online education (May to June 2020). Then we conducted text mining, namely, keyword frequency analysis, network analysis, word cloud analysis, and sentiment analysis were performed. The implications derived as a result of the analysis can help education policy makers and educators working in the field to improve online education quality and establish its future directions.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.29 no.2
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• pp.85-108
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• 2018

This study aimed to analyze media reports of the Comprehensive Library Advancement Plan using big data analysis in order to determine trends and implications by period. To do so, related data from 2009 to 2017 were collected from major domestic web portal sites. Words in the collected data were refined through the text mining process and frequency, centrality, and structural equivalence analyses were performed. Results confirmed that, during the implementation of the first and the second phases of the Comprehensive Library Advancement Plan, the focus of the library policy changed from external growth to strengthening internal stability and advancement of library operation, and the media coverage were limited to specific policies such as expansion of library facilities. Findings from this study will serve as useful material for ascertaining the approach to perceive and understand the national library policy represented by the Comprehensive Library Advancement Plan.