• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.453-462
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• 2016

Since the Internet provides a way of expressing and sharing Internet users' mindsets, corporate marketers want to acquire measurable and actionable insights from web data. In the past, companies used to analyze the attitude, satisfaction, and loyalty of consumers toward their brands using survey data, whereas nowadays this is done using the big data extracted from Social Network Services. In this study, we propose a framework for clustering brand names using the social metrics gathered on social media. We also conduct a case study of the automobile industry to verify the feasibility of the proposed framework. We calculate the brand name distance for each pair of brand names based on the total number of times that they are mentioned together. These distances are used to project the brand name onto a 3-dimensional space using multidimensional scaling. After the projection, we found the clusters of brand names and identified the characteristics of each cluster. Furthermore, we concluded this paper with a discussion of the limitations and future directions of this research.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.101-106
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• 2016

A number of non-structured data associated with lectures in the field of university education have been generated and it is an important consideration of the students's written comments lecture evaluation. The purpose of this study is to find student interaction factors associated with the student evaluation of teaching at universities, and to provide some insights into improving the student evaluation program based on the results. So, this study consists of three steps that create interaction score, collect student's written comments satisfaction, and analyze an individual professor score. There are a number of limitations to this study. The limitation is that the study was conducted on a narrow sample of the overall student population.

• Journal of the Korea Convergence Society
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• v.11 no.5
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• pp.199-207
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• 2020

Recently, big data analysis has drawn attention in various fields as it can generate value from large amounts of data and is also used to run political campaigns or predict results. However, existing research had limitations in compiling information about candidates at a high-level by analyzing only specific SNS data. Therefore, this study analyses news trends, topics extraction, sentiment analysis, keyword analysis, comment analysis for the 2017 presidential election of South Korea. The results show that various topics had been generated, and online opinions are extracted for trending keywords of respective candidates. This study also shows that portal news and comments can serve as useful tools for predicting the public's opinion on social issues. This study will This paper advances a building strategic course of action by providing a method of analyzing public opinion across various fields.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.5
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• pp.279-284
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• 2017

Currenty, in the era of big data, text mining and opinion mining have been used in many domains, and one of their most important research issues is to extract significant information from social media. Thus in this paper, we propose a logistic regression ensemble method of finding the main body text from blog HTML. First, we extract structural features and text features from blog HTML tags. Then we construct a classification model with logistic regression and ensemble that can decide whether any given tags involve main body text or not. One of our important findings is that the main body text can be found through 'depth' features extracted from HTML tags. In our experiment using diverse topics of blog data collected from the web, our tag classification model achieved 99% in terms of accuracy, and it recalled 80.5% of documents that have tags involving the main body text.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.5
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• pp.483-490
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• 2019

Due to the high complexity and diversity of the problems of the future society, it is getting harder to solve with the traditional single technology. In recent years, there has been a growing interest in convergence technology, which combines or connects different types of technologies to create new technologies and industries. In this study, we explored the convergence R&D area of ICT technology related to landslide prevention/response. It is true that the world has been exposed to various disasters due to recent climate change. As a result, there is a tendency to use Big Data and ICT for disaster preparedness and recovery. Especially, in the case of landslides, it is a natural disaster that requires research not only to study actual landslides but also to predict potential landslides. Therefore, in this study, we analyzed what kind of convergence R&D is being carried out in the field of ICT for preventing and responding to landslide. Therefore, in this study, Web of Science article data were analyzed by using the scientometric analysis and 51 landslide-related ICT convergence R&D areas were derived.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.113-121
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• 2019

The fourth industrial revolution technology focused on the fusion of infrastructure and various advanced technologies related city. Therefore, technical cooperation in various fields of research is essential. In order to activating the fourth industrial revolution technologies, it is necessary to research the state of technology in various fields. Consequently, this paper aims to analysis of domestic and foreign research trends on technology to the fourth industrial revolution using SNA and text mining for web site. We collected text, date data of research paper and report in web site for five years, that is, from January 1st in 2014 to December 31st in 2018. Next, we have deduced the major keywords in public data through analyzing the morphemes. Then we have analyzed the core and related keyword lists through an SNA. In Korea, the focus is on R&D and legal/institutional solution in relation to the fourth industrial revolution technology. On the other hand, in the case of foreign, there was focus on practical technologies for urban services in detail aspects.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.145-155
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• 2022

North Korean defectors are experiencing economic hardship due to the prolonged COVID-19 outbreak. In order to solve this problem, interest in starting a business is increasing. This study targeted the current and previous government, and discovered major topics through text mining of news data on North Korean defector starting a business to examine the start-up support policies according to the keynote of the present regime. Additionally, key factors for successful start-ups were derived through interviews with North Korean defectors who have done them. As a result of the analysis, it is necessary to focus on women and the youth, and to actively expand specialized entrepreneurship education and financial support for North Korean defectors. In addition, it was confirmed that there is a need for a practical and continuous entrepreneurship education program.

• Journal of dental hygiene science
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• v.24 no.1
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• pp.9-21
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• 2024

Background: The purpose of this study was to utilize text network analysis and topic modeling to identify interconnected relationships among keywords present in patent information related to oral health, and subsequently extract latent topics and visualize them. By examining key keywords and specific subjects, this study sought to comprehend the technological trends in oral health-related innovations. Furthermore, it aims to serve as foundational material, suggesting directions for technological advancement in dentistry and dental hygiene. Methods: The data utilized in this study consisted of information registered over a 20-year period until July 31st, 2023, obtained from the patent information retrieval service, KIPRIS. A total of 6,865 patent titles related to keywords, such as "dentistry," "teeth," and "oral health," were collected through the searches. The research tools included a custom-designed program coded specifically for the research objectives based on Python 3.10. This program was used for keyword frequency analysis, semantic network analysis, and implementation of Latent Dirichlet Allocation for topic modeling. Results: Upon analyzing the centrality of connections among the top 50 frequently occurring words, "method," "tooth," and "manufacturing" displayed the highest centrality, while "active ingredient" had the lowest. Regarding topic modeling outcomes, the "implant" topic constituted the largest share at 22.0%, while topics concerning "devices and materials for oral health" and "toothbrushes and oral care" exhibited the lowest proportions at 5.5% each. Conclusion: Technologies concerning methods and implants are continually being researched in patents related to oral health, while there is comparatively less technological development in devices and materials for oral health. This study is expected to be a valuable resource for uncovering potential themes from a large volume of patent titles and suggesting research directions.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.311-321
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• 2024

This study comprehensively analyzed the perceptions of Chinese consumers who have and have not purchased Samsung smartphones, based on data from the social media platform Weibo. Various big data analysis techniques were used, including text mining, frequency analysis, centrality analysis, semantic network analysis, and CONCOR analysis. The results indicate that positive perceptions of Samsung smartphones include aspects such as design aesthetics, camera functionality, AI features, screen quality, specifications, and performance, and their status as a premium brand. On the other hand, negative perceptions include issues with pricing, a yellow tint in photos, slow charging speeds, and safety concerns. These findings will provide a crucial basis for making significant improvements in Samsung's market strategy in China.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.