• The Journal of Information Systems
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• v.31 no.1
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• pp.289-304
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• 2022

Purpose The development of the network and mobile industries has induced companies to invest in information systems, leading a new industrial revolution. The Journal of Information Systems, which developed the information system field into a theoretical and practical study in the 1990s, retains a 30-year history of information systems. This study aims to identify academic values and research trends of JIS by analyzing the trends. Design/methodology/approach This study aims to analyze the trend of JIS by compounding various methods, named as TLS mining analysis. TLS mining analysis consists of a series of analysis including Term Frequency-Inverse Document Frequency (TF-IDF) weight model, Latent Dirichlet Allocation (LDA) topic modeling, and a text mining with Semantic Network Analysis. Firstly, keywords are extracted from the research data using the TF-IDF weight model, and after that, topic modeling is performed using the Latent Dirichlet Allocation (LDA) algorithm to identify issue keywords. Findings The current study used the summery service of the published research paper provided by Korea Citation Index to analyze JIS. 714 papers that were published from 2002 to 2012 were divided into two periods: 2002-2011 and 2012-2021. In the first period (2002-2011), the research trend in the information system field had focused on E-business strategies as most of the companies adopted online business models. In the second period (2012-2021), data-based information technology and new industrial revolution technologies such as artificial intelligence, SNS, and mobile had been the main research issues in the information system field. In addition, keywords for improving the JIS citation index were presented.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.213-218
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• 2012

Modern culture has describing the year's situation along the social environment. A literary work changed as if the year's situation change. Therefore we can understand the age through the literary work and get knowledge the social request of the year's. This literary works have made a chance to know approaching more closely to user as producing media resources. Recently, IT convergence and digital convergence become a new trend to combine each other academic area and get much synergy effect. In this paper, we propose an application and design of the ontology that needs to make digital content from modern literary work's information. For this works, we specify the structure of the year's literary work and the relation of each factor. The specification method used topic map. Each relation model was specified the connection by topic vector.

• Journal of the Korean Society of Earth Science Education
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• v.6 no.1
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• pp.69-77
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• 2013

The study aims to figure out pre-service elementary teachers' knowledge understanding on 'stratum and rock' as well as teaching-learning types on the same topic. A total of 65 seniors in an advanced science education course at B University of Education joined the research to fulfill the purpose above. With PCK classification framework, the study examined pre-service teachers' knowledge understanding on 'stratum and rock' while it analyzed how the teachers would teach the given topic to students. The results of the study are presented as follows. First, it was observed that the pre-service elementary teachers have a great understanding on 'stratum and rock' that would be taught via a science textbook for elementary fourth graders. However, regarding terms in 'shale and limestone', they appeared to have a relatively short understanding. Second, PCK elements of the pre-service teachers related to 'stratum and rock' were analyzed and according to the results, the teachers would be interested in teaching model selecting in the teaching-learning strategy field while they would be well aware of how important it is for them to perform an experiment in a teaching process. The teachers also appeared to understand that the teacher question can be mutual complementary during class. However, it turned out that the teachers would have a very much low understanding on learners' prior knowledge as they particularly believe that learning could be significantly affected by the learners' perception level as well as their learning interest and motive. Third, the pre-service elementary teachers were told to design teaching plans on 'stratum and rock' so that the study could find out what learning-teaching methods the teachers would adopt to teach the topic. It was learned that the teachers would proceed with the class basically by giving the learners a descriptive explanation on the topic and also by using pictures and drawings to enhance the learners' understanding during the class.

• The Journal of Information Systems
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• v.30 no.4
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• pp.277-301
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• 2021

Purpose The purpose of this study is to analyze the sentiment responses of the general public to non-face-to-face work using text mining methodology. As the number of non-face-to-face complaints is increasing over time, it is difficult to review and analyze in traditional methods such as surveys, and there is a limit to reflect real-time issues. Approach This study has proposed a method of the research model, first by collecting and cleansing the data related to non-face-to-face work among tweets posted on Twitter. Second, topics and keywords are extracted from tweets using LDA(Latent Dirichlet Allocation), a topic modeling technique, and changes for each section are analyzed through DTM(Dynamic Topic Modeling). Third, the complaints of non-face-to-face work are analyzed through the classification of positive and negative polarity in the COVID-19 section. Findings As a result of analyzing 1.54 million tweets related to non-face-to-face work, the number of IDs using non-face-to-face work-related words increased 7.2 times and the number of tweets increased 4.8 times after COVID-19. The top frequently used words related to non-face-to-face work appeared in the order of remote jobs, cybersecurity, technical jobs, productivity, and software. The words that have increased after the COVID-19 were concerned about lockdown and dismissal, and business transformation and also mentioned as to secure business continuity and virtual workplace. New Normal was newly mentioned as a new standard. Negative opinions found to be increased in the early stages of COVID-19 from 34% to 43%, and then stabilized again to 36% through non-face-to-face work sentiment analysis. The complaints were, policies such as strengthening cybersecurity, activating communication to improve work productivity, and diversifying work spaces.

• Korean Journal of Forensic Psychology
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• v.13 no.1
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• pp.23-52
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• 2022

In this study, focused group interviews with 24 incumbent judges were conducted on how they conceptualize public opinion and what attitude they take toward it in relation to judicial trials. The contents of the interviews were analyzed through grounded theory and topic modeling (STM). According to the grounded theory results, judges distinguished concepts such as social rules, socially accepted ideas, legal emotion, and public mood from public opinion, and subdivided public opinion into temporary and emotional reactions to specific legal cases and consistent attitudes toward law and policies. In addition, it was found that judges' attitudes toward public opinion and social norms differed depending on the type of cases or legal issues. Topic modeling results significantly corresponded to the grounded theory results. In this model, the effects of the types of cases dedicated to participants on topical prevalence were statistically significant.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.255-265
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• 2024

To address environmental challenges and improve energy efficiency, the adoption of electric vehicles has led to a surge in related research. However, to comprehensively understand the research trends within the field of electric vehicles, it is necessary to systematically analyze vast amounts of data. This study systematically analyzed research trends in the field of electric vehicles and identified key research topics through LDA topic modeling, based on 36,519 papers related to electric vehicles collected from the SCIE database. The data analysis revealed a total of 10 major topics, of which three were identified as hot topics showing an upward trend: Electric Vehicle Charging Infrastructure, Energy and Environmental Policy, and Optimization and Algorithms. Conversely, five topics were identified as cold topics exhibiting a downward trend: Battery Temperature and Cooling, Battery Materials and Chemistry, Motor and Mechanical Design, Control Strategies and Systems, and Battery Components and Materials. This study provides basic data for understanding the current research trends in electric vehicles and offers valuable information for researchers in selecting research topics related to electric vehicles.

• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.249-268
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• 2024

Information literacy is a necessary competency for all people living in the information society, but undergraduate students are especially in need of information literacy in the process of academic performance and career preparation. In this study, we conducted frequency analysis, network analysis, and topic modeling on the English abstracts of information literacy-related research on undergraduate students listed in KCI to identify trends in information literacy research on undergraduate students. The main keywords and subsequent research topics were derived by analyzing the frequency analysis and keyword network and comparing the results, and eight subtopics were derived from the topic modeling to observe the main research areas. Information literacy for college students was mainly studied for educational purposes, and nursing information and analysis model development were the main subtopics.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• The Korean Journal of Archival Studies
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• no.19
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• pp.57-102
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• 2009

Recently, distribution of record via web and coefficient of utilization are increase. so, Archival information service using website becomes essential part of record center. The main point of archival information service by website is making record information retrieval easy. It has need of matching user's request and representation of record resources correctly to making archival information retrieval easy. Archivist and record manager have used various information representation tools from taxonomy to recent thesaurus, still, the accuracy of information retrieval has not solved. This study constructed record retrieval system based on Topic Map by modeling record resources which focusing on description metadata of the records to improve this problem. The target user of the system is general web users and its range is limited to the president related sources in the National Archives Portal Service. The procedure is as follows; 1) Design an ontology model for archival information service based on topic map which focusing on description metadata of the records. 2) Buildpractical record retrieval system with topic map that received information source list, which extracted from the National Archives Portal Service, by editor. 3) Check and assess features of record retrieval system based on topic map through user interface. Through the practice, relevance navigation to other record sources by semantic inference of description metadata is confirmed. And also, records could be built up as knowledge with result of scattered archival sources.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.99-115
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• 2022

In this study, domestic academic journal papers on tax compliance, tax consciousness, and faithful tax payment (hereinafter referred to as "tax compliance") were comprehensively analyzed from an interdisciplinary perspective as a representative research topic in the field of tax science. To achieve the research purpose, topic modeling technique was applied as part of text mining. In the flow of data collection-keyword preprocessing-topic model analysis, potential research topics were presented from tax compliance related keywords registered by the researcher in a total of 347 papers. The results of this study can be summarized as follows. First, in the keyword analysis, keywords such as tax investigation, tax avoidance, and honest tax reporting system were included in the top 5 keywords based on simple term-frequency, and in the TF-IDF value considering the relative importance of keywords, they were also included in the top 5 keywords. On the other hand, the keyword, tax evasion, was included in the top keyword based on the TF-IDF value, whereas it was not highlighted in the simple term-frequency. Second, eight potential research topics were derived through topic modeling. The topics covered are (1) tax fairness and suppression of tax offenses, (2) the ideology of the tax law and the validity of tax policies, (3) the principle of substance over form and guarantee of tax receivables (4) tax compliance costs and tax administration services, (5) the tax returns self- assessment system and tax experts, (6) tax climate and strategic tax behavior, (7) multifaceted tax behavior and differential compliance intentions, (8) tax information system and tax resource management. The research comprehensively looked at the various perspectives on the tax compliance from an interdisciplinary perspective, thereby comprehensively grasping past research trends on tax compliance and suggesting the direction of future research.