• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.229-236
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• 2016

In the past, crime prediction methods utilized previous records to accurately predict crime occurrences. Yet these crime prediction models had difficulty in updating immense data. To enhance the crime prediction methods, some approaches used social network service (SNS) data in crime prediction studies, but the relationship between SNS data and crime records has not been studied thoroughly. Hence, in this paper, we analyze the relationship between SNS data and criminal occurrences in the perspective of crime prediction. Using Latent Dirichlet Allocation (LDA), we extract tweets that included any words regarding criminal occurrences and analyze the changes in tweet frequency according to the crime records. We then calculate the number of tweets including crime related words and investigate accordingly depending on crime occurrences. Our experimental results demonstrate that there is a difference in crime related tweet occurrences when criminal activity occurs. Moreover, our results show that SNS data analysis will be helpful in crime prediction model as there are certain patterns in tweet occurrences before and after the crime.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.75-81
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• 2019

The purpose of this study was to perform a convergence study for the investigation of the trend of research topics related to thyroid cancer in Korea. We collected related research papers from DBpia and employed LDA-based topic model. In result, we identified four research topics, each of which concerns "Surgery", "Disease aggressiveness", "Survival analysis", and "Well-being of patients". With multinomial logistic regression, we found significant time trend, where "Surgery"-related topic was popular before 2000, topics regarding "Disease aggressiveness" and "Survival analysis" were frequently addressed in the 2000s, and "Survival analysis" and especially "Well-being of patients" have been pursued since 2010. The findings would serve as a reference guide for research directions. Future work may examine whether the recent change in research topics is observed in other diseases.

• Trans-
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• v.11
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• pp.97-116
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• 2021

This study aims to suggest an environment in which audience can play the role of micro-influencers after exploring the factors of satisfaction focusing on audiences who attended the Seoul Dance Film Festival(SeDaFF). In order to meet the research goal, among the audiences who attended SeDaFF, articles mentioning this festival on their SNS were collected and this data was analyzed using the LDA topic model. As a result, the most important satisfaction factor when visiting a dance film festival was the program. It might seem cliché to discuss the importance of programs at film festivals, but through the examination, this study made the case that if the satisfaction factor is met, it is still possible to influence the behavioral intentions and reinforcing the role of a micro-influencer even in a genre with a strong artistic nature and a limit to audience development. Furthermore, this study was intended to contribute to broadening the scope of research on the audience.

• Smart Media Journal
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• v.10 no.4
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• pp.102-110
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• 2021

Currently, the advanced construction technology endorsement system is being operated to promote the development of domestic construction technology. We tried to examine the implicit meanings inherent in advanced construction technologies by analyzing the relationship between emerging vocabularies with high importance in relation to the advanced construction technologies endorsed through this system. For this purpose, 918 cases of advanced construction technology information were collected. Based on the endorsed year and summary of the advanced construction technologies, the importance of the emerging vocabularies was measured for each advanced construction technology. And, based on the LDA model, the degree of influence between related vocabularies was evaluated for each of the four topic areas. Topics according to the technical application fields were analyzed. From 1990 to 2021, the trend of changes in highly influential vocabularies by each topic was inferred. In the future, changes in the degree of influence of the topics of environment, machinery, facilities, and maintenance and reinforcement of structures and related technology fields were predicted.

• Knowledge Management Research
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• v.23 no.2
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• pp.37-60
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• 2022

The growth of virtual reality games and the popularization of blockchain technology are bringing significant changes to the formation of the metaverse industry ecosystem. Especially, after Meta acquired Oculus, a VR device and application company, the growth of VR-based metaverse services is accelerating. In this study, the concept that supports leisure activities in the metaverse environment is explored realting to game-like features in VR apps, which differentiates traditional mobile apps based on a smart phone device. Using exploratory text mining methods and network analysis approches, 241 apps registed in the Oculus Quest 2 App Store were analyzed. Analysis results from a quasi-network show that a leisure concept is closely related to various genre features including a game and tourism. Additionally, the anlaysis results of G & F model indicate that the leisure concept is distictive in the view of gateway brokerage role. Those results were also confirmed in LDA topic modeling analysis.

• Korea Trade Review
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• v.48 no.1
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• pp.29-53
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• 2023

This study analyzes the statements made by representatives of member countries at the WTO Ministerial Conference (MC), the highest decision-making body of the WTO, to examine the position and attitude that Korea has shown at the WTO during the last 27 years. After constructing text dataset by extracting about 1,800 statement documents made by member countries from the WTO document database, the text mining technique is applied to figure out the characteristics of Korea's statements compared to other member countries. Through formal characteristics such as the number of remarks and length of speech, basic attitudes such as continuity of Korea's interest in the WTO and the level of interest in the WTO are measured. In terms of substantive characteristics, the topics in the statements of Korea are categorized through the LDA topic model, and the keywords of Korea for each session are analyzed through comparative analysis with statements by other member countries.

• Journal of Biomedical Engineering Research
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• v.33 no.2
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• pp.65-71
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• 2012

In this paper, the Gaussian Mixture Model(GMM) which is very robust modeling for pattern classification is proposed to classify wrist motions using surface electromyograms(EMG). EMG is widely used to recognize wrist motions such as up, down, left, right, rest, and is obtained from two electrodes placed on the flexor carpi ulnaris and extensor carpi ulnaris of 15 subjects under no strain condition during wrist motions. Also, EMG-based feature is derived from extracted EMG signals in time domain for fast processing. The estimated features based in difference absolute mean value(DAMV) are used for motion classification through GMM. The performance of our approach is evaluated by recognition rates and it is found that the proposed GMM-based method yields better results than conventional schemes including k-Nearest Neighbor(k-NN), Quadratic Discriminant Analysis(QDA) and Linear Discriminant Analysis(LDA).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.604-625
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• 2018

Nowadays, SMS spam has been overflowing in many countries. In fact, the standards of filtering SMS spam are different from country to country. However, the current technologies and researches about SMS spam filtering all focus on dividing SMS message into two classes: legitimate and illegitimate. It does not conform to the actual situation and need. Furthermore, they are facing several difficulties, such as: (1) High quality and large-scale SMS spam corpus is very scarce, fine categorized SMS spam corpus is even none at all. This seriously handicaps the researchers' studies. (2) The limited length of SMS messages lead to lack of enough features. These factors seriously degrade the performance of the traditional classifiers (such as SVM, K-NN, and Bayes). In this paper, we present a new fine categorized SMS spam corpus which is unique and the largest one as far as we know. In addition, we propose a classifier, which is based on the probability topic model. The classifier can alleviate feature sparse problem in the task of SMS spam filtering. Moreover, we compare the approach with three typical classifiers on the new SMS spam corpus. The experimental results show that the proposed approach is more effective for the task of SMS spam filtering.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.99-107
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• 2009

This paper presents an enhanced hybrid model based on Independent Component Analysis(ICA) in order to features of numeric characters in license plates. ICA which is used only in high dimensional statistical features doesn't consider statistical features in low dimension and correlation between numeric characters. To overcome the drawbacks of ICA, we propose an improved ICA with the hybrid model using both Principle Component Analysis(PCA) and Linear Discriminant Analysis(LDA). Experiment results show that the proposed model has a superior performance in feature extraction and recognition compared with ICA only as well as other hybrid models.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.795-806
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• 2021

Recently, as text data through online channels have become vast, there is a growing interest in research that summarizes and analyzes them. One of the fundamental analyses of text data is to extract potential topics. Although the researcher may read all the data and summarize the contents one by one, it is not easy to deal with large amounts of data. Blei and Lafferty (2007) and Blei et al. (2003) proposed topic modeling methods for extracting topics using a statistical model. Since the text data is generally collected over time, it is worthwhile to monitor the topic's changes. In this study, we propose a topic index based on the results of the topic model. In addition, a control chart, a representative tool for statistical process management, is applied to monitor the topic index over time. As a practical example, we use text data collected from Blue House National Petition boards between March 5, 2018, and March 5, 2020.