• Journal of Research and Publication Ethics
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• v.5 no.2
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• pp.1-6
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• 2024

Purpose: PBM is emerging as a major management system for securing corporate productivity and enhancing competitiveness, and various studies are being conducted. The purpose of this study is to analyze research trends published in KCI-listed journals and papers since 1999 to understand the current status of research and provide basic data for more extensive research and development of performance management in the future. Research design, data and methodology: A detailed examination of research trends was conducted through the analysis of abstracts from 154 research papers on PBM. To facilitate a comprehensive analysis of these trends, LDA topic modelling was employed. Results: First, it should be noted that research on PBM is not limited to the area of HRM. Instead, PBM research is expanding to encompass comprehensive personnel systems. Second, the results of topic modeling analysis show that although the initial focus of research was on human resource management, there is now a growing interest in fairness and organizational culture in the entire organization. Conclusions: PBM is becoming a dominant paradigm as it shifts from HR systems to organizational fairness and culture. This suggests that future research should consider both quantitative and qualitative aspects of PBM to improve corporate performance while prioritizing organizational fairness and culture.

• Journal of Distribution Science
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• v.22 no.2
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• pp.21-30
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• 2024

Purpose: This study explores the development of zakat distribution studies and the integration of existing studies. This study is expected to complement a systematic literature review in the field of zakat distribution to inspire further research directions. Research design, data, and methodology: This research method uses a systematic literature review assisted by the Nvivo application and the PRISMA system, which selects from 427 articles to 53 articles to be analyzed based on publication and classification of the theme of its findings. This study describes publications, authors, themes, cited articles, and research themes. Results: This study shows the dominance of Malaysian writers and significant developments in 2020. In addition, the study shows the most popular articles based on the most citations and word cloud analysis. The primary topics of zakat distribution publications are management strategy, development, the zakat institution, and the recipient. Conclusions: The study advises that Future research could focus on zakat distribution's asnaf characteristics. Next, a study on administration expenses and scalability concerns in zakat collection and distribution planning can avoid wasting cash. This topic hinders zakat institutions' distribution services.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.883-890
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• 2020

In this paper, we propose a variational expectation-maximization algorithm that computes posterior probabilities from Latent Dirichlet Allocation (LDA) model. The algorithm approximates the intractable posterior distribution of a document term matrix generated from a corpus made up by 50 papers. It approximates the posterior by searching the local optima using lower bound of the true posterior distribution. Moreover, it maximizes the lower bound of the log-likelihood of the true posterior by minimizing the relative entropy of the prior and the posterior distribution known as KL-Divergence. The experimental results indicate that documents clustered to image classification and segmentation are correlated at 0.79 while those clustered to object detection and image segmentation are highly correlated at 0.96. The proposed variational inference algorithm performs efficiently and faster than Gibbs sampling at a computational time of 0.029s.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.1
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• pp.7-14
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• 2002

In power system study, relibility assessment has been an important topic during past several decards because sudden power interruption can bring about enormous economic loss. although the size of a substation is smaller than that of generation system or transmission system, switching actions after fault(s) make reliability assessment of substation rather complex situations such as switching actions easily and permit various probability distributions in describing substation elements. Despite this ability of Monte Carlo simulation, one-parameter exponential distribution is still popular in this reliability assessment. This paper examines the characteristics of several two-parameter probability distributions, and offers new parameter decision rule based on average and variance of the target to be modelled. In case study, this paper shows the profits by using Weibull distribution which is one of two-parameter probabilistic distributions instead of exponential one.

• Journal of Computing Science and Engineering
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• v.6 no.2
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• pp.143-150
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• 2012

Computational techniques for topic classification can support qualitative research by automatically applying labels in preparation for qualitative analyses. This paper presents an evaluation of supervised learning techniques applied to one such use case, namely, that of labeling emotions, instructions and information in suicide notes. We train a collection of one-versus-all binary support vector machine classifiers, using cost-sensitive learning to deal with class imbalance. The features investigated range from a simple bag-of-words and n-grams over stems, to information drawn from syntactic dependency analysis and WordNet synonym sets. The experimental results are complemented by an analysis of systematic errors in both the output of our system and the gold-standard annotations.

• Asian Journal of Business Environment
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• v.5 no.1
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• pp.13-26
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• 2015

Purpose - While studying the topic of seeking behavioral excellence (proactive and creative competence) in organization, scholars have presented a variety of literature sprinkled with countless theories on behavioral competence. Research design, data, and methodology - Foremost among the several theories on this topic are two distinct sets of behavioral theories: OCB (Organizational Citizenship Behavior) and Constructive Deviance. Both of these theories attempt to posit their usefulness in positive organizational outcomes (such as organizational effectiveness, quality, performance, and innovations). Results - However, their proposed constructs are opposing and studies are conducted in isolation, thereby creating a significant literature gap and omitting the possibility of being inclusive of the best that both OCB and constructive deviance have to offer. Conclusions - The article bridges the gap by critically examining OCB and constructive deviance and including a consideration of other empirical studies in an attempt to be comprehensive while, at the same time, seeking to find an effective behavioral construct that is both appropriate and conducive for positive organizational outcomes in the context of the current business environment.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.4
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• pp.315-334
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• 2013

This study performs opinion mining of newspaper articles, based on topics extracted by topic modeling. We analyze the attitudes of the news media towards a major issue of 'presidential election', assuming that newspaper partisanship is a kind of opinion. We first extract topics from a large collection of newspaper texts, and examine how the topics are distributed over the entire dataset. The structure and content of each topic are then investigated by means of network analysis. Finally we track down the chronological distribution of the topics in each of the newspapers through time serial analysis. The result reveals that both the liberal newspapers and the conservative newspapers exhibit their own tendency to report in line with their adopted ideology. This confirms that we can count on opinion mining technique based on topics in order to analyze opinion in a reliable fashion.

• Journal of Korean Society of Rural Planning
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• v.27 no.3
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• pp.103-116
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• 2021

In this study, the characteristics of rural tourism image perceived by urban residents were analyzed through text analysis of blog data. In order to examine the images related to rural tourism, blog data written with the keyword "Gochang-gun travel" was used. LDA topic analysis, one of the text mining techniques, was used for the analysis. In the tourism image of Gochang-gun, 9 topics were derived, and 112 major places appeared. This was divided into 3 main activities and 5 object spaces through the review of keywords and the original text of blog data. As a result of the analysis, the traditional main resources of the region, Seonun mountain, Seonun temple, and Gochang-eup fortress, formed topic. On the other hand, world heritage such as dolmen and Ungok wetland did not appear as topic. In particular, the farms operated by the private sector form individual topics, and the theme farm can be seen as an important resource for tourism in Gochang-gun. Also, through the distribution of place keywords, it was possible to understand the characteristics of travel by region and the usage behavior of visitors. In the case of Gochang-gun, there was a phenomenon in which visitors were biased by region. This seems to be the result of Gochang-gun seeking to vitalize local tourism focusing on natural, ecological, and scenic resources. It is necessary to establish a plan for balanced regional development and develop other types of tourism resources. This study is different in that it identified the types and characteristics of rural tourism images in the region perceived by visitors, and the status of tourism at the regional level.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.343-349
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• 2022

We intend to use topic modeling to identify radiation-themed papers published from 1989 to 2022 and analyze the relevance and weight between topics. This study analyzed topics derived from national subjects for 717 papers published until recently in 2022 to contribute to the revitalization of research in the field of radiation. Through text mining, overall research trends on the subject distribution of the study were analyzed, and five topics were derived through topic modeling. First, among the papers to be analyzed, a total of 1,675 words were frequency-analyzed through the preprocessing process of key words in a total of 717 papers centered on keywords. Second, as a result of analyzing topics based on the association of constituent words for five topics, it was found that studies focused on minimizing dose in the range that does not degrade image quality in the fields of radiation, image, CT clinical. In addition, it was found that various studies were mainly conducted in the MRI, and the study of ultrasound in various areas of disease analysis was actively attempted.