• Journal of Information Technology Services
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• v.18 no.4
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• pp.135-149
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• 2019

Recently, there are many researches for extracting meaningful information from large amount of text data. Among various applications to extract information from text, topic modeling which express latent topics as a group of keywords is mainly used. Topic modeling presents several topic keywords by term/topic weight and the quality of those keywords are usually evaluated through coherence which implies the similarity of those keywords. However, the topic quality evaluation method based only on the similarity of keywords has its limitations because it is difficult to describe the content of a topic accurately enough with just a set of similar words. In this research, therefore, we propose topic keywords reorganizing method to improve the identifiability of topics. To reorganize topic keywords, each document first needs to be labeled with one representative topic which can be extracted from traditional topic modeling. After that, classification rules for classifying each document into a corresponding label are generated, and new topic keywords are extracted based on the classification rules. To evaluated the performance our method, we performed an experiment on 1,000 news articles. From the experiment, we confirmed that the keywords extracted from our proposed method have better identifiability than traditional topic keywords.

• Journal of Information Technology Services
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• v.22 no.2
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• pp.93-127
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• 2023

This study analyzed the CEO New Year's addresses of major Korean companies, extracting key topics for employees via text mining techniques. An intended contribution of this study is to assist reporters, analysts, and researchers in gaining a better understanding of the New Year's addresses by elucidating the implicit and implicative features of messages within. To this end, this study collected and analyzed 545 New Year's addresses published between 2012 and 2021 by the top 66 Korean companies in terms of market capitalization. Research methodologies applied include text clustering, word embedding of keywords, frequency analysis, and topic modeling. Our main findings suggest that the messages in the New Year's addresses were categorized into nine topics-organizational culture, global advancement, substantial management, business reorganization, capacity building, market leadership, management innovation, sustainable management, and technology development. Next, this study further analyzed the managerial significance of each topic and discussed their characteristics from the perspectives of time, industry, and corporate groups. Companies were typically found to emphasize sound management, market leadership, and business reorganization during economic downturns while stressing capacity building and organizational culture during market transition periods. Also, companies belonging to corporate groups tended to emphasize founding philosophy and corporate culture.

• The Journal of the Korea Contents Association
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• v.17 no.4
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• pp.331-341
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• 2017

This paper aims to analyze English vocabulary of the North Korean textbooks diachronically using the constructed English textbook corpus. The North Korea English textbooks attained from Information Center on North Korea of the Ministry of Unification are divided into before and after Kim Jong-Il era for the year of 1996 in which the curriculum revision has been conducted. They are stored as text files to analyse vocabularies using WordSmith Tools 7.0. The vocabulary size of the revised textbooks increased after the curriculum reorganization, but the number of vocabulary types and vocabulary diversity decreased. After the curriculum revision, it was found that lots of vocabulary related to the establishment of the Kim Jong-Il system appeared as the keyword. It was also found that some vocabularies reflected the economic and social life of North Korea. In addition, through comparison of the 100 high-frequency word list and keywords, it can be concluded that the vocabulary of the English textbooks of North Korea is gradually changing into communicative contents from contents related with written language.

• Journal of East Asia Management
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• v.4 no.1
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• pp.1-22
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• 2023

This study conducted a big data analysis study through text mining and semantic network analysis to explore the perception of defense reform 2.0. The collected data were analyzed with the top 70 keywords as the appropriate range for network visualization. Through word frequency analysis, connection centrality analysis, and an N-gram analysis, we identified issues that received much attention such as troop reduction, shortening of military service period, dismantling of the border area unit, and returning wartime operational control. In particular, the results of clustering words through CONCOR analysis showed that there was a great interest in pursuing the technical group, concerns about military capacity reduction, and reorganization of manpower structure. The results of the analysis through text mining techniques are as follows. First, it was found that there was a lack of awareness about measures to reinforce the reduced troops while receiving much attention to the reduction of troops in Defense Reform 2.0. Second, it was found that it is necessary to actively communicate with the local community due to the deconstruction and movement of the border area units, such as the decrease of the population of the region and the collapse of the local commercial area. Third, it was judged that it is necessary to show substantial results through the promotion of barracks culture and the defense industry, which showed that there was less interest than military structure and defense operation from the people and the introduction of active policies. Through this study, we analyzed the public's interest in defense reform 2.0, which is a representative defense policy, and suggested a plan to draw support for national policy.

• 대한공업교육학회지
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• v.41 no.2
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• pp.48-68
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• 2016

This study was conducted to identify the NCS-related research trends, Keywords, the Keywords Networks and the extension of the Keywords using the sementic network analysis and to seek for the development plans about NCS. For this, the study searched 345 the papers, with the National Competency Standards or NCS as a key word, among master's theses, dissertations and scholarly journals that RISS provides, and selected a total of 345 papers. Annual frequency analysis of the selected papers was carried out, and Semantic Network Analysis was carried out for 68 key words which can be seen as key terms of the terms shown by the subject. The method of analysis were KrKwic software, UCINET6.0 and NetDraw. The study results were as follows: First, NCS-related research increased gradually after starting in 2002, and has been accomplishing a significant growth since 2014. Second, as a result of analysis of keyword network, 'NCS, development, curriculum, analysis, application, job, university, education,' etc. appeared as priority key words. Third, as a result of sub-cluster analysis of NCS-related research, it was classified into four clusters, which could be seen as a research related to a specific strategy for realization of NCS's purpose, an exploratory research on improvement in core competency and exploration of college students' possibility related to employment using NCS, an operational research for junior college-centered curriculum and reorganization of the specialized subject, and an analysis of demand and perception of a high school-level vocational education curriculum. Fourth, the connection forming process among key words of domestic study results about NCS was expanding in the form of 'job${\rightarrow}$job ability${\rightarrow}$NCS${\rightarrow}$education${\rightarrow}$process, curriculum${\rightarrow}$development, university${\rightarrow}$analysis, utilization${\rightarrow}$qualification, application, improvement${\rightarrow}$plan, operation, industry${\rightarrow}$design${\rightarrow}$evaluation.'