• CELLMED
• /
• v.13 no.2
• /
• pp.3.1-3.21
• /
• 2023

In this study, the drinking culture of the Choseon dynasty is examined with the text frequency analysis technique on the entire 『Choseonwangjosilok (朝鮮王朝實錄)』. This study examined a total of 1,968 volumes and 948 books about 27 kings of Choseon , which spans a total of 518 years, through web crawling on the National Institute of Korean History website. Python 3.8 was used to extract sentences related to alcohol, Rhino 1.4.5 was used for morphological analysis to extract nouns, and Gephi 0.9.2 was used for semantic network analysis. According to 『Choseonwangjosilok (朝鮮王朝實錄)』 about alcohol culture, the results of the analysis are as follow: Alcoholic beverages were more often used in court or in ritual ceremonies rather than those based on specific ingredients or manufacturing methods commonly used by the general public. regarding the ruling class through semantic network analysis l in the 『Choseonwangjosilok (朝鮮王朝實錄)』, the Choseon dynasty was found to be highly associated with political issues related to maintaining the power relations within the Korean royal court system. At times, alcohol was used to maintain personal relationships, while at other times it was seen as an essential item in state ceremonies. It was also used as a highly political means to maintain and strengthen national power.

• Journal of Korean Academy of Nursing
• /
• v.47 no.6
• /
• pp.806-816
• /
• 2017

Purpose: As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis. Methods: The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network. Results: A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality. Conclusion: This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.

• Journal of People, Plants, and Environment
• /
• v.24 no.5
• /
• pp.451-460
• /
• 2021

Background and objective: The prolonged COVID-19 pandemic has had significant impacts on mental health, which has emerged as a major public health issue around the world. This study aimed to analyze trends and network structure of 'grow-your-own (GYO)' through Instagram, one of the most influential social media platforms, to encourage and sustain home gardening activities for promotion of emotional support and physical health. Methods: A total of 6,388 posts including keyword hashtags '#gyo' and '#growyourown' on Instagram from June 13, 2020 to April 13, 2021 were collected. Word embedding was performed using Word2Vec library, and 7 clusters were identified with K-means clustering: GYO, garden and gardening, allotment, kitchen garden, sustainability, urban gardening, etc. Moreover, we conducted social network analysis to determine the centrality of related words and visualized the results using Gephi 0.9.2. Results: The analysis showed that various combinations of words, such as #growourrownfood, #growourrownveggies, and #growwhatyoueat revealed preference and interest of users in GYO, and appeared to encourage their activities on Instagram. In particular, #gardeningtips, #greenfingers, #goodlife, #gardeninglife, #gardensofinstagram were found to express positive emotions and pride as a gardener by sharing their daily gardening lives. Users were participating in urban gardening through #allotment, #raisedbeds, #kitchengarden and we could identify trends toward self-sufficiency and sustainable living. Conclusion: Based on these findings, it is expected that the trend data of GYO, which is a form of urban gardening, can be used as the basic data to establish urban gardening plans considering each characteristic, such as the emotions and identity of participants as well as their dispositions.

• Korean Journal of Environment and Ecology
• /
• v.34 no.3
• /
• pp.259-270
• /
• 2020

Plants Social Network (PSN) was first used in recent studies to incorporate the plant sociology methods for the understanding of plant society with the social network analysis methods that have recently attracted attention in the social science and visualize and analyze a PSN. The process of construction and analysis on PSN proceeds in the order of setting up the survey area, investigating the appearance plants species on plots of 100㎡, analyzing the interspecific association, building the sociogram, and analyzing the network structure and centrality. This study established a PSN by investigating the appearance species after installing 708 plots to include various dominant vegetational physiognomies in Busan Metropolitan City, where coastal and inland vegetation could be observed simultaneously. The survey found a total of 195 species, including 42 species of evergreen, 151 species of deciduous trees, and 2 species of semi-evergreen trees. The interspecies binding analysis was performed with the focus on the total number of species. It showed the number of friendly species in the order of Eurya japonica (47 species), Trachelospermum asiaticum (46 species), Linder glauca (44 species), Sorbus alnifolia (44 species), and Ligustrum japonicum (41 species). Based on it, we generated a sociogram using Gephi 0.9.2 program. The sociogram was divided into groups that appeared mostly on the coast and those that did not, reflecting the geographical distribution characteristics of forest vegetation in Busan. The analysis of the network structured showed 1,709 links and an average of 17.5 species having interspecies binding with a species. The density was 0.09, the diameter was 5, and the average path distance was 2.268. We concluded that various PSNs should be established in the future for precise comparative analysis of network characteristics in the social science field. In the PSN of Busan Metropolitan City, Eurya japonica, Linder glauca, Ligustrum japonicum, and Trachelospermum asiaticum showed high centrality.

• Korean Journal of Environment and Ecology
• /
• v.36 no.6
• /
• pp.651-661
• /
• 2022

Plants social network analysis, which combines plants society and social network analyses, is a new research method for understanding plants society. This study was conducted to investigate the relationship between species, using plant social network analysis targeting Taejongdae in Busan, and build basic data for management. Taejongdae, located in the warm temperate forest in Korea, is a representative coastal forest of Busan Metropolitan City, and the Pinus thunbergii-Eurya japonicacommunity is widely distributed. This study set up 100 quadrats (size of 100m²each) in Taejongdae to investigate the species that emerged and analyzed the interspecies association focusing on major species. Based on the results, a sociogram was created using the Gephi 0.9.2, and the network centrality and structure were analyzed. The results showed that the frequency of appearance was high in the order of P. thunbergii, E. japonica, Quercus serrata, Sorbus alnifolia, Ligustrum japonicum, and Styrax japonicusand that many evergreen broad-leaved trees appeared due to the environmental characteristics of the site. The plants social network of Taejongdae was composed of a small-scale network with 50 nodes and 172 links and was divided into 4 groups through modularization. The succession sere identified through a sociogram confirmed that the group that include P. thunbergiiand E. japonicawould progress to a deciduous broadleaf community dominated by Q. serrataand Carpinus tschonoskii, using hub nodes such as Prunus serrulataf. spontaneaand Toxicodendron trichocarpum. Another succession sere was highly likely to progress to an evergreen broad-leaved community dominated by Machilus thunbergiiand Neolitsea sericea, using M. thunbergiias a medium. In some areas, a transition to a deciduous broad-leaved community dominated by Celtis sinensis, Q. variabilisand Zelkova serratausing Lindera obtusilobaand C. sinensisas hub nodes was expected.

• Journal of Korea Port Economic Association
• /
• v.39 no.2
• /
• pp.15-23
• /
• 2023

In a situation where the necessity and importance of ESG management is increasing recently, it is judged that selecting important ESG-related variables for container terminals, which are the bases of export and import logistics, among various variables of ESG evaluation agencies will help to establish ESG management strategies for container terminals which led us to proceed with this study. The results of word network analysis are summarized as follows. The weighed degree, that is, the AWD of Environmental management(E) variables, is obtained in the order of Environmental Protection Investment(54), Environmental Awareness Education(45), Work Team Structure(31), Environmental certification(32). Page Ranks, the order of centrality and connectivity index is Environmental Awareness Education(0.0765), Employee Engagement(0.0765), Environmental Protection Investment(0.0761), Work Team Composition(0.0761), and Environmental certification(0.0761). The AWD(Average Weighed Degree) of the Social Responsibility Management(S) variables, followed by Protecting workers' human rights and contributing to local communities(68), Safety Education(63), Safety certification(59), and Responding to infectious diseases(40). Orders by Page Ranks, centrality and connectivity Index, are Protecting workers' human rights and contributing to local communities(0.165), Safety Education(0.153), Safety Certification(0.144) and Responding to infectious diseases(0.102). The AWD of Governance and Ethical management(G) variables, followed by Anti-corruption(27), Transparent management(24), Mutual cooperation between stakeholders(19), and Sustainability reporting(9). Page Ranks, the order of centrality and connectivity index is the Anti Corruption(0.241), Transparent management(0.216), Mutual cooperation between stakeholders(0.174), Directors' roles and responsibilities(0.105), Shareholder protection(0.097) and Sustainability Report(0.096).

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.69-92
• /
• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of the Korean Society for Library and Information Science
• /
• v.50 no.2
• /
• pp.289-307
• /
• 2016

Infectious diseases such as Ebola virus disease become a social issue and draw public attention to be a major topic on news or research. As a result, there have been a lot of studies on infectious diseases using text-mining techniques. However, there is no research on content analysis of two media channels that have distinct characteristics. Accordingly, in this study, we conduct topic analysis between news (representing a social perspective) and academic research paper (representing perspectives of bio-professionals). As text-mining techniques, topic modeling is applied to extract various topics according to the materials, and the word co-occurrence map based on selected bio entities is used to compare the perspectives of the materials specifically. For network analysis, topic map is built by using Gephi. Aforementioned approaches uncovered the difference of topics between two materials and the characteristics of the two materials. In terms of the word co-occurrence map, however, most of entities are shared in both materials. These results indicate that there are differences and commonalties between social and academic materials.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.133-142
• /
• 2023

National parks are one of the important types of protected area management systems established by IUCN and a management model for implementing effective conservation and sustainable use of natural and cultural heritage in countries around the world, and they assume important roles in conservation, scientific research, education, recreation and driving community development. In the context of big data, this study takes China's Liaohe National Park, a typical representative of global coastal wetlands, as a case study, and using Python technology to collect tourists' travelogues and reviews from major OTA websites in China as a source. The text spans from 2015 to 2022 and contains 2998 reviews with 166,588 words in total. The results show that wildlife resources, natural landscape, wetland ecology and the fishing and hunting culture of northern China are fully reflected in the perceptions of visitors to Liaohe National Park; visitors have strong positive feelings toward Liaohe National Park, but there is still much room for improvement in supporting services and facilities, public education and visitor experience and participation.