• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.8 no.3
• /
• pp.531-541
• /
• 2018

This study researched text mining and text network analysis to analyze the images of Nonghyup Insurance for consumers. With the recent development of social media, many texts are being produced and reproduced, and texts of social media provide important information to companies. Text mining and text network analysis are used in many studies to identify image of company and product. As a result of the text analysis, the positive image of the Nonghyup Insurance is safety and stability. Negative images of the Nonghyup Insurance is concern and anxiety. As a result of the textual network analysis, Centered mage of Nonghyup Insurance is safety and concern. This paper allows researchers to extract several lessons learned that are important for the text mining and text network analysis.

• Journal of the Korean Society for information Management
• /
• v.31 no.4
• /
• pp.49-68
• /
• 2014

The purpose of this study is to perform content analysis of research articles using the language network analysis method in Korea and catch the basic point of the language network analysis method. Six analytical categories are used for content analysis: types of language text, methods of keyword selection, methods of forming co-occurrence relation, methods of constructing network, network analytic tools and indexes. From the results of content analysis, this study found out various features as follows. The major types of language text are research articles and interview texts. The keywords were selected from words which are extracted from text content. To form co-occurrence relation between keywords, there use the co-occurrence count. The constructed networks are multiple-type networks rather than single-type ones. The network analytic tools such as NetMiner, UCINET/NetDraw, NodeXL, Pajek are used. The major analytic indexes are including density, centralities, sub-networks, etc. These features can be used to form the basis of the language network analysis method.

• Journal of the Korean Society for Library and Information Science
• /
• v.55 no.3
• /
• pp.397-417
• /
• 2021

As review text contains the experience and opinions of the customers, analyzing review text helps to understand the subject. Existing studies either only used sentiment analysis on online restaurant reviews to identify the customers' assessment on different features of the restaurant or network analysis to figure out the customers' preference. In this study, we conducted both sentiment analysis and network analysis on the review text of the restaurants with high star ratings and those with low star ratings. We compared the review text of the two groups to distinguish the difference of the two and identify what makes great restaurants great.

• Journal of Korean Library and Information Science Society
• /
• v.47 no.4
• /
• pp.1-18
• /
• 2016

The purpose of this study is to explore application of topic modeling for topic analysis of book report. Topic modeling can be understood as one method of topic analysis. This analysis was conducted with texts in 23 book reports using LDA function of the "topicmodels" package provided by R. According to the result of topic modeling, 16 topics were extracted. The topic network was constructed by the relation between the topics and keywords, and the book report network was constructed by the relation between book report cases and topics. Next, Centrality analysis was conducted targeting the topic network and book report network. The result of this study is following these. First, 16 topics are shown as network which has one component. In other words, 16 topics are interrelated. Second, book report was divided into 2 groups, book reports with high centrality and book reports with low centrality. The former group has similarities with others, the latter group has differences with others in aspect of the topics of book reports. The result of topic modeling is useful to identify book reports' topics combining with network analysis.

• Journal of Korean Library and Information Science Society
• /
• v.53 no.3
• /
• pp.169-191
• /
• 2022

The purpose of this study is to attempt a text network analysis for two years of 『Reference Materials for Librarian』 (2016-2017) published by the Library Operation Methodology Research Institute in North Korea. A text network analysis can measure how important a particular word by grasping the connectivity and relationship between words beyond a simple word frequency analysis, and it is also possible to interpret specific social phenomena and derive implications. Frequency, degree centrality, the betweenness centrality, community analysis of the collected words were calculated using NetMiner. As a result, the terms 'users', 'information services', 'information needs', 'information technology', 'social learning', 'computers', 'databases', 'information acquisition', 'information retrieval' and 'librarian' were appeared as important ones in understanding North Korean libraries.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2018.07a
• /
• pp.526-529
• /
• 2018

본 연구에서는 텍스트 네트워크분석을 활용하여 국방분야 연구의 핵심 주제어와 연구주제를 분석하고 이를 통해 전체 지식구조를 파악하고자 하였다. 이를 위해 2010년부터 2017년까지의 국방대학교 학위과정 논문을 대상으로 국방분야 연구현황을 진단하고 지식구조를 구성하였다. 8년간 누적된 논문 710건의 초록을 분석하여 총 6,883개의 단어를 추출한 후, 단어의 논문 등장 빈도수와 단어간 링크수를 파레토 법칙에 따라 상위 20%의 기준으로 총 270개의 단어로 추출하였고, 컴포넌트 분석을 통해 최종 170개의 핵심 주제어를 도출하였다. 이 핵심 주제어를 통해 중심성 분석과 응집구조를 분석하여, 국방분야에 대한 총 6개의 지식구조 그룹을 도출하였다.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.1
• /
• pp.111-123
• /
• 2022

In the era of the Covid19, container shipping rates are surging up. Many studies have attempted to investigate the factors affecting a surge in container shipping rates. However, there is limited literature using text mining techniques for analyzing the underlying causes of the surge. This study aims to identify the factors behind the unprecedented surge in shipping rates using network text analysis and LDA topic modeling. For the analysis, we collected the data and keywords from articles in Lloyd's List during past two years(2020-2021). The results of the text analysis showed that the current surge is mainly due to "US-China trade war", "rising blanking sailings", "port congestion", "container shortage", and "unexpected events such as the Suez canal blockage".

• Journal of Convergence for Information Technology
• /
• v.9 no.3
• /
• pp.68-74
• /
• 2019

The purpose of this study was to identify research trends of 892 domestic articles (2009-2018) related to emotional labor by using text-mining and network analysis. To this end, the keyword of these papers were collected and coded and eventually converted to 871 nodes and 2625 links for network text analysis. First, network text analysis revealed that the top four main keyword, according to co-occurrence frequency, were burnout, turnover intention, job stress, and job satisfaction in order and that the frequency and the top four core keyword by degree centrality were all relatively the high. Second, based on the top four core keyword of degree centrality the ego network analysis was conducted and the keyword for connection centroid of each network were presented.

• Journal of the Korea Convergence Society
• /
• v.12 no.9
• /
• pp.295-305
• /
• 2021

The purpose of this study is to explore the trend of nursing professional research topics published domestically through quantitative content analysis. The research method performed procedures for collecting academic papers, refining and extracting words, and data analysis. A text network was developed by collecting 351 papers and extracting words from the abstract, and network analysis and topic modeling were performed. The core-topics were nurses, nursing professionalism, nursing students, nursing care, professional self-concept, health care professionals, satisfaction, clinical competence, and self-efficacy. Through topic modeling, topic groups of nurse's professionalism, nursing students' professionalism, nursing professional identity, and nursing competency were identified. Over time, core-topics remained unchanged, but topics such as role conflict and ethical values in the 1990s, self-leadership and socialization in the 2000s, and clinical practice stress and support systems in the 2010s have emerged. In conclusion, it is necessary to facilitate multidimensional interventional research to improve nursing professionalism of clinical nurses and nursing students.

• The Journal of the Korea Contents Association
• /
• v.18 no.1
• /
• pp.19-30
• /
• 2018

The main purposes of this study are to analyze the contents and text network of online tourism information. For this purpose, Jeju Island, one of the representative tourist destinations in South Korea is selected as a study site. And this study collects the contents of both JeJu official tourism website and Sina Weibo's personal blogs which is one of the most popular Social Network Systems in China. In addition, this study analyzes this online text information using ROST Content Mining System, one of the Chinese big data mining systems. The results of the content analysis show that the formal website of Jeju includes the nouns related to natural, geographical and physical resources, verbs related to existence of resources, and adjectives related to the beauty, cleanness and convenience of resources mainly. Meanwhile, personal blogs include the nouns of Korean-wave, food, local products, other destinations and shopping, verbs related to activity and feeling in Jeju, and adjectives related to their experiences and feeling mainly. Finally, the results of text network show that there are some strong centrality and network of online tourism information at formal website, but there are weak relationships in personal blogs. The results of this study may be able to contribute to the development of demand-based marketing strategies of tourists destination.