• Journal of KIISE:Databases
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• v.37 no.6
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• pp.343-350
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• 2010

We can get answers we want to know via questioning in Knowledge Search Service (KSS) based on Q&A Community. However, it is getting more difficult to find credible documents in enormous documents, since many anonymous users regardless of credibility are participate in answering on the question. In previous works in KSS, researchers evaluated the quality of documents based on textual information, e.g. recommendation count, click count and non-textual information, e.g. answer length, attached data, conjunction count. Then, the evaluation results are used for enhancing search performance. However, the non-textual information has a problem that it is difficult to get enough information by users in the early stage of Q&A. The textual information also has a limitation for evaluating quality because of judgement by partial factors such as answer length, conjunction counts. In this paper, we propose the QualityRank algorithm to improve the problem by textual and non-textual information. This algorithm ranks the relevant and credible answers by considering textual/non-textual information and user centrality based on Social Network Analysis(SNA). Based on experimental validation we can confirm that the results by our algorithm is improved than those of textual/non-textual in terms of ranking performance.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.21-29
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• 2018

In the era of the 4-th industrial revolution, the concept of smart factory is emerging. There are efforts to predict the occurrences of facility errors which have negative effects on the utilization and productivity by using data analysis. Data composed of the situation of a facility error and the type of the error, called the facility error log, is required for the prediction. However, in many manufacturing companies, the types of facility error are not precisely defined and categorized. The worker who operates the facilities writes the type of facility error in the form with unstructured text based on his or her empirical judgement. That makes it impossible to analyze data. Therefore, this paper proposes a framework for constructing a phrase network to support the identification and classification of facility error types by using facility error logs written by operators. Specifically, phrase indicating the types are extracted from text data by using dictionary which classifies terms by their usage. Then, a phrase network is constructed by calculating the similarity between the extracted phrase. The performance of the proposed method was evaluated by using real-world facility error logs. It is expected that the proposed method will contribute to the accurate identification of error types and to the prediction of facility errors.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.9-27
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• 2019

The purpose of this study is to examine consumers' responses to mobile payment services by using a text-mining technique focusing on Samsung Pay as it is used in both online and offline transactions. We conducted text frequency analysis, text clustering analysis, and text network analysis using R programming. The major findings are as follows. First, the most frequently used key words referenced the brand names of the mobile devices, the replacement of traditional wallets and unique functions of Samsung Pay. Second, there was a clear split between positive and negative responses at the macro level. Third, replacement of traditional wallets played a great role in the positive responses and continuous use of mobile payment services. This study provides in-depth understanding of consumer responses toward mobile payment services. It also offers practical implications that may help mobile payment marketers correspond to consumer values and expectations, thus increasing consumer satisfaction.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.145-156
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• 2022

In recent years, as there is growing concern with ESG (Environmental, Social, and Governance), the strategic direction of business management is changing from maximizing shareholders wealth to maximizing stakeholders value. ESG is reshaping a corporation's supply chain management strategies. The purpose of this study is to explore the ESG challenges in supply chain management. As a result of network text analysis and topic modeling analysis on 3226 news articles, 'Suppliers', 'Sustainability', 'Shared Growth' 'Carbon Neutral', 'Safety and Health', 'Responsible Business Alliance', 'Supply Chain Due Diligence Law' were identified as the main issue. Since ESG initiatives in the supply chain are not limited to the efforts of individual firms, future research should focus on figuring out what difficulties and challenges exist in the diffusion of ESG practices along multi-tiered supply chains, and how to overcome them.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.550-555
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• 2018

This study aims to identify and compare contents and keywords used in articles related to blockchain applications to various industries. The text mining and Semantic Network Analysis, as methods of keyword network analysis, were used to analyze articles including terms of 'finance' 'energy' and 'logistics', which media and government frequently mentioned as areas that can apply blockchain technologies. For this study, data were collected from 43,093 articles from January, 2017 through July, 2018. Data crawling was carried out by using Python BeautifulSoup and data cleaning was performed in order to eliminate mutual redundancies of the three terms. After that, text mining and semantic network analysis were performed using Textom and UCInet for network analysis between keywords. The results showed that all the three terms were similar in terms of 'technology', but there were differences in the contents of 'government policy' or 'industry' issues. In addition, there were differences in frequencies and centralities of these terms.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.259-260
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• 2019

이 연구는 텍스트마이닝 및 사회연결망 분석을 통하여 지금까지 축적된 연구주제의 핵심어와 네트워크 지식구조를 확인하여 무용경영 연구의 흐름과 동향을 분석하는데 목적이 있다. 무용경영 연구동향에 관한 텍스트마이닝 분석 결과, 전반적으로 무용경영 연구에서 가장 높은 빈도를 나타낸 특정 토픽으로는 'Performing arts', 'Entrepreneurship', 'Dance', 'Audience development', 'Dance management' 등이 도출되었다. 사회연결망 분석을 실시한 결과, 'Entrepreneurship', 'Dance Marketing', 'Marketing'에서 노드간의 연결성이 높은 것으로 나타났다. 또한 국외에서는 꾸준히 관객개발(audience development)과 공연마케팅(performing arts marketing)이 주요 쟁점으로 다루어져 왔다. 이와 같은 연구동향 및 지식구조 분석을 토대로 이 연구는 보다 확장된 무용경영 연구의 관점을 제안하였다.

• Journal of East-West Nursing Research
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• v.26 no.2
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• pp.118-129
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• 2020

Purpose: The purpose of this study was to analyze the topic trend of domestic simulation nursing education research using text network analysis(TNA). Methods: This study was conducted in four steps. TNA was performed using the NetMiner (version 4.4.1) program. Firstly, 245 articles from 4 databases (RISS, KCI, KISS, DBpia) published from 2008 to 2018, were collected. Secondly, keyword-forms were unified and representative words were selected. Thirdly, co-occurrence matrices of keywords with a frequency of 2 or higher were generated. Finally, social network-related measures-indices of degree centrality and betweenness centrality-were obtained. The topic trend over time was visualized as a sociogram and presented. Results: 178 author keywords were extracted. Keywords with high degree centrality were "Nursing student", "Clinical competency", "Knowledge", "Critical thinking", "Communication", and "Problem-solving ability." Keywords with high betweenness centrality were "CPR", "Knowledge", "Attitude", "Self-efficacy", "Performance ability", and "Nurse." Over time, the topic trends on simulation nursing education have diversified. For example, topics such as "Neonatal nursing", "Obstetric nursing", "Pediatric nursing", "Blood transfusion", "Community visit nursing", and "Core basic nursing skill" appeared. The core-topics that emerged only recently (2017-2018) were "High-fidelity", "Heart arrest", "Clinical judgment", "Reflection", "Core basic nursing skill." Conclusion: Although simulation nursing education research has been increasing, it is necessary to continue studies on integrated simulation learning designs based on various nursing settings. Additionally, in simulation nursing education, research is required not only on learner-centered educational outcomes, but also factors that influence educational outcomes from the perspective of the instructors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.34-36
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• 2022

In the existing text sentiment analysis models, the entire text is usually directly modeled as a whole, and the hierarchical relationship between text contents is less considered. However, in the practice of sentiment analysis, many texts are mixed with multiple emotions. If the semantic modeling of the whole is directly performed, it may increase the difficulty of the sentiment analysis model to judge the sentiment, making the model difficult to apply to the classification of mixed-sentiment sentences. Therefore, this paper proposes a sentiment analysis model BHGCN that considers the text hierarchy. In this model, the output of hidden states of each layer of BERT is used as a node, and a directed connection is made between the upper and lower layers to construct a graph network with a semantic hierarchy. The model not only pays attention to layer-by-layer semantics, but also pays attention to hierarchical relationships. Suitable for handling mixed sentiment classification tasks. The comparative experimental results show that the BHGCN model exhibits obvious competitive advantages.

• Information Systems Review
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• v.18 no.1
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• pp.141-155
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• 2016

This study identified terminologies related to the proximity and frequency of disaster by social network analysis (SNA) and text mining, and then expressed the outcome into a mind map. The termdocument matrix of text mining was utilized for the terminology proximity analysis, and the SNA closeness centrality was calculated to organically express the relationship of the terminologies through a mind map. By analyzing terminology proximity and selecting disaster response-related terminologies, this study identified the closest field among all the disaster response fields to disaster response and the core terms in each disaster response field. This disaster response terminology analysis could be utilized in future core term-based terminology standardization, disaster-related knowledge accumulation and research, and application of various response scenario compositions, among others.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.13-28
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• 2015

In accordance with importance of the economic value of forests and forest use, the paradigm of development and use has emerged as the dominant paradigm of forest policy. As forests are recognized as an important means of Wellness, the government pursues a policy convergence between forest use and conservation. So, this article analyzed whether the change of forestry convergence paradigm is reflected in policy or not. The purpose of this study was to analyze through content analysis and network analysis, whether the new combined text value are fused in how forest policy. According to the results, the function of utilization which is off the traditional forestry industry and recreation, wellness are acquired a greater importance in the 5th plan than 4th plan. But the 5th plan is insufficient to establish of foundation for forestry management and welfare functions. The evidence suggest a sign of sustained paradigm convergency in forest policy of Korea. As the policy implication in establishing national forest master plan, it is necessary to strengthen policy capability to pursue sustainable forestry utilization, which can converge forest use and conservation.