• 한국섬유공학회:학술대회논문집
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• 한국섬유공학회 2007년도 학술발표회 논문집
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• pp.109-110
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• 2007

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• 한국동물학회:학술대회논문집
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• 한국동물학회 2000년도 한국생물과학협회 학술발표대회
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• pp.261.2-262
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• 2000

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• 한국자원식물학회:학술대회논문집
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• 한국자원식물학회 2007년도 추계학술발표회
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• pp.344.2-344.2
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• 2007

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• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.391-406
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• 2015

Social media have emerged as new communication channels between consumers and companies that generate a large volume of unstructured text data. This social media content, which contains consumers' opinions and interests, is recognized as valuable material from which businesses can mine useful information; consequently, many researchers have reported on opinion-mining frameworks, methods, techniques, and tools for business intelligence over various industries. These studies sometimes focused on how to use opinion mining in business fields or emphasized methods of analyzing content to achieve results that are more accurate. They also considered how to visualize the results to ensure easier understanding. However, we found that such approaches are often technically complex and insufficiently user-friendly to help with business decisions and planning. Therefore, in this study we attempt to formulate a more comprehensive and practical methodology to conduct social media opinion mining and apply our methodology to a case study of the oldest instant noodle product in Korea. We also present graphical tools and visualized outputs that include volume and sentiment graphs, time-series graphs, a topic word cloud, a heat map, and a valence tree map with a classification. Our resources are from public-domain social media content such as blogs, forum messages, and news articles that we analyze with natural language processing, statistics, and graphics packages in the freeware R project environment. We believe our methodology and visualization outputs can provide a practical and reliable guide for immediate use, not just in the food industry but other industries as well.

• 디지털산업정보학회논문지
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• 제14권2호
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• pp.61-67
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• 2018

This media conversion in which webtoons are remediated to movies and dramas has taken off since the mid-2000s. Webtoons may be favorable for media conversion into movies and dramas as the story as finished and has proven to be fun with a fixed readership: however, only a small number of webtoon were successful box office hits or received high viewer ratings. Then in 2017, the movie based on the webtoon, succeed in attracting more more then 10 million viewers. In this regard, this study derived the success factors by comparing and analyzing the narrative structure and visual elements of , which was the biggest hit movie, with the original webtoon. The case analysis showed that there are two necessary elements for success: a text configuration of strategy optimized for media conversion, which is based on understanding the different media characteristics of webtoon and movie; and a configuration strategy that exaggerates the personalities of the characters and compresses the story of a webtoon which features various events and many characters in a long series, in consideration of the characteristics of movie which needs to give a big impact in 2 hours.

• 한국IT서비스학회지
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• 제13권4호
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• pp.275-287
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• 2014

Recently, the volume of unstructured text data generated by various social media has been increasing rapidly; consequently, the use of text mining to support decision-making has also been growing. In particular, academia and industry are paying significant attention to topic analysis in order to discover the main issues from a large volume of text documents. Topic analysis can be regarded as static analysis because it analyzes a snapshot of the distribution of various issues. In contrast, some recent studies have attempted to perform dynamic issue tracking, which analyzes and traces issue trends during a predefined period. However, most traditional issue tracking methods have a common limitation : when a new period is included, topic analysis must be repeated for all the documents of the entire period, rather than being conducted only on the new documents of the added period. Additionally, traditional issue tracking methods do not concentrate on the transition of individuals' interests from certain issues to others, although the methods can illustrate macro-level issue trends. In this paper, we propose an individual interests tracking methodology to overcome the two limitations of traditional issue tracking methods. Our main goal is not to track macro-level issue trends but to analyze trends of individual interests flow. Further, our methodology has extensible characteristics because it analyzes only newly added documents when the period of analysis is extended. In this paper, we also analyze the results of applying our methodology to news articles and their access logs.

• Journal of Information Processing Systems
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• 제16권2호
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• pp.277-300
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• 2020

This paper presents a method for clustering short text documents, such as news headlines, social media statuses, or instant messages. Due to the characteristics of these documents, which are usually short and sparse, an appropriate technique is required to discover hidden knowledge. The objective of this paper is to identify the combination of document representation, document distance, and document clustering that yields the best clustering quality. Document representations are expanded by external knowledge sources represented by a Distributed Representation. To cluster documents, a K-means partitioning-based clustering technique is applied, where the similarities of documents are measured by word mover's distance. To validate the effectiveness of the proposed method, experiments were conducted to compare the clustering quality against several leading methods. The proposed method produced clusters of documents that resulted in higher precision, recall, F1-score, and adjusted Rand index for both real-world and standard data sets. Furthermore, manual inspection of the clustering results was conducted to observe the efficacy of the proposed method. The topics of each document cluster are undoubtedly reflected by members in the cluster.

• 방송공학회논문지
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• 제26권5호
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• pp.592-598
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• 2021

현 이동통신 시스템은 재난경보 방송을 위하여 CBS 프로토콜을 이용한다. 그러나 CBS 프로토콜 표준은 제한된 텍스트 문자의 전송만을 규정하고 있다. 따라서 국내 문자에 익숙하지 않은 외국인 등은 수신된 메시지를 충분히 이해하기 어려운 단점이 있다. 또한, 현 CBS는 메시지의 글자수 제한으로 자세한 정보를 전송하기 어려운 한계를 가진다. 이러한 어려움을 극복하기 위하여 본 논문에서는 CBS 재난문자 전송시 단말기에서 이미지 정보 와 URL을 표출하는 기법을 제안한다. 이 기법은 5G 이동통신을 위한 CBS의 메시지 구조를 이용한다. 이 메시지 구조를 이용할 경우 CBS 프로토콜은 문자 코딩방식에 관계없이 단말기에서 이미지 정보와 URL을 표출할 수 있음을 보인다.

• 한국IT서비스학회지
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• 제18권1호
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• pp.45-61
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• 2019

Blockchain technology, which enables transparent transactions among individuals without central control, opens up diverse business possibilities. It is also expected that blockchain will have a ripple effect on the entire area of society including finance, manufacturing, distribution, and the public sector. Previous studies related to the blockchain also deals with its functional features and application to industrial and public fields. In the new technology such as blockchain, it is necessary to know what social perception is in order to create technological development environment, but there is a lack of research on it. Therefore, this study aims to find out the implications for industrial and policy direction by analyzing issues related to the blockchain in South Korea and the US through text mining. From these two countries, we collected text data related to blockchain in online communities and internet articles. Then, we did co-occurrence analysis and topic modeling on them respectively. As a result of this study, we have found common points and differences in keywords and topics extracted from social media in the two countries. Based on them, we can offer helpful suggestions for building a sound blockchain ecosystem, and directions for future research.

• 한국의류산업학회지
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• 제24권5호
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• pp.545-556
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• 2022

To overcome the pandemic, a new strategy for innovation is in demand throughout the value chains of the fashion industry that emphasize the importance of fashion technology. Accordingly, as various viewpoints and fields of debate are unfolding to consider the direction of change led by fashion technology, it is necessary to make an active value judgment precedent by understanding the differences between various opinions. This study aims to derive keywords from fashion technology used during the pandemic, to infer the characteristics of each type of perspective and to understand their characteristics. For the research, this study combines text mining analysis and content analysis. Text mining analysis is used to find statistical patterns by collecting keywords from big data from online media, and content analysis is used to interpret the data qualitatively. After analyzing the results of this study, the following observations are made. First, the perspective of positive acceptance seeks to maximize the perception and sensory action of fashion through technology; this amplifies experience, an opportunity for innovation and efficiency. Second, critical vigilance highlights the side effects of radical changes in fashion technology, characterized by concerns about capital-centered polarization, threats to human rights, and infringement of creative thinking. Lastly, the perspective of gradual adoption is the gradual convergence of technologies, characterized by the pursuit of an appropriate balance.