• 한국IT서비스학회지
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• 제16권4호
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• pp.161-176
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• 2017

Recently, most of the users can easily get access to a variety of information sources about companies, products, and services through online channels. Therefore, the online user evaluations are becoming the most powerful tool to generate word of mouth. The user's evaluation is provided in two forms, quantitative rating and review text. The rating is then divided into an overall rating and a detailed rating according to various evaluation criteria. However, since it is a burden for the reviewer to complete all required ratings for each evaluation criteria, so most of the sites requested only mandatory inputs for overall rating and optional inputs for other evaluation criteria. In fact, many users input only the ratings for some of the evaluation criteria and the percentage of missed ratings for each criteria is about 40%. As these missed ratings are the missing values in each criteria, the simple average calculation by ignoring the average 40% of the missed ratings can sufficiently distort the actual phenomenon. Therefore, in this study, we propose a methodology to predict the rating for the missed values of each criteria by analyzing user's evaluation information included the overall rating and text review for each criteria. The experiments were conducted on 207,968 evaluations collected from the actual hotel evaluation site. As a result, it was confirmed that the prediction accuracy of the detailed criteria ratings by the proposed methodology was much higher than the existing average-based method.

• 한국BIM학회 논문집
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• 제5권1호
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• pp.8-15
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• 2015

With the emergence of environment-friendly paradigms, many countries around the world have enacted various laws to take care of environmental pollution-related problems. The goal of these environmental laws and regulations was to properly respond to rapid environmental pollution. Because of the simultaneous enactment of these laws on diverse pollution sources, however, a variety of problems, including an unclear correlation among these laws, have occurred. As a result, workers have found it hard to collect and use the related laws and regulations. Therefore, this study proposes a metadata-based information retrieval method for the efficient search of environment-friendly laws and regulations. The laws and regulations were structured using metadata from users, business stage, topic and department. These were obtained through semantic analysis on environment-friendly laws and regulations, and then an intelligent retrieval approach was utilized. To verify the retrieval plan, a test case was conducted, and improvement in retrieval accuracy against the conventional system was confirmed. It appears that the proposed plan will improve productivity in the construction industry by improving accuracy in retrieving environment-friendly laws and regulations.

• Human Ecology Research
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• 제55권6호
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• pp.609-623
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• 2017

Fur apparel is a representative luxury item that displays wealth and social status; however, it is also recognized as an unethical product criticized for its animal maltreatment in the production process. Understanding consumer responses to an ambivalent object, such as fur apparel, is an important research topic both academically and practically. This study investigates the hierarchical effects of perceived benefits and risks of fur apparel on consumers' emotions and purchase intention by applying the ABC model of attitudes to identify the mediating effects of consumer emotions. An online survey was conducted on 390 female consumers that verified hypotheses through structural equation modeling and bootstrapping analysis using phantom variables. The initial results of the survey showed that the relationship between perceived conspicuous benefits and purchase intention towards fur apparel was partially mediated by positive emotion. Second, the relationship between perceived epistemic benefits and purchase intention was completely mediated by positive emotion. Third, the relationship between perceived ethical risk and purchase intention was completely mediated by positive and negative emotions. Fourth, perceived social risk did not affect the purchase intention of fur apparel significantly. The results support that cognitive beliefs about the subject have a significant positive effect on behavioral intentions through emotions as suggested in the ABC model of the attitude. This study provides an in-depth understanding of consumer responses to ambivalent objects by revealing the individual mediating effects of consumers' positive and negative emotions.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.997-1007
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• 2021

The last decade has seen the emergence of the idea of "sharing economy" as people are more aware of environmental issues. Although clothing businesses applying the model of sharing consumption have emerged recently, less research effort has been invested in this topic, especially in investigating young consumers' intention. The purpose of this study is to investigate factors driving Generation Z consumers' behavioral intention toward online fashion rental. In this research, a conceptual framework is proposed based on the Theory of Planned Behavior and Technology Acceptance Model. To test the research model and hypotheses, a survey of 375 students and pupils was conducted in Vietnam. All the scales' reliability and validity were assessed through Cronbach's Alpha and confirmatory factor analysis. Structural equation modeling was used to assess the relationship among constructs. The study results showed that attitude toward behavior, subjective norm and perceived behavioral control were positive contributors to Gen Z's intention to use online fashion rental. Besides, the positive relationships between attitude and two other factors - perceived usefulness and perceived ease of use - were also highlighted. Moreover, the findings provided empirical evidence for supporting the positive impact of interpersonal influence, e-WOM, and influencer e-marketing on subjective norm.

• 미래기술융합논문지
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• 제2권3호
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• pp.1-7
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• 2023

전세계적으로 기후 변화 문제가 심화되고 있으며, 화석연료의 사용 증가로 인한 다양한 문제들이 야기되고 있다. 특히 탄소중립 정책이 가속화됨으로 재생에너지에 대한 관심이 증가하고 있는 추세이다. 이에 본 연구에서는 SCOPUS DB를 활용하여 신재생에너지 연구 동향을 파악하였다. 초록 제공이 가능한 1,353개의 데이터를 확보하고 이를 분석할 수 잇도록 데이터 전처리를 수행하고 분석을 수행하였다. 토픽모델링 분석 결과 중요한 키워드로 renewable와 enegy로 나타났다. 이 외에도 electricity, solar, wind, 등이 중요한 키워드로 분석이 되었다. 본 연구 결과를 통해 신재생에너지 관련 기업의 실무자들이 신재생에너지 관련 연구동향을 실무에 활용할 수 있을 것으로 기대한다.

• 4차산업연구
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• 제3권2호
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• pp.11-17
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• 2023

Purpose - Recently, in the field of language education, interest in edutech has increased due to difficulties in classroom teaching due to COVID-19. Accordingly, we would like to analyze research topics related to e-learning before and after COVID-19 and examine the implications for the future Korean language education field. Research design, data, and methodology - This study organized a list of papers to be analyzed by searching for e-learning terms applicable to Korean language education in RISS. The collected data was electronically documented, keywords were extracted using text mining techniques, and word frequencies were checked, and then viewed through cloud visualization. Result - It was confirmed that research on e-learning in the field of Korean language education has increased rapidly in 2021 and 2022. In particular, extensive research on online learning methods has been actively conducted due to the difficulties of face-to-face learning in the COVID-19 era. There have been many studies on teaching and learning methods, such as flipped learning, hybrid learning, blended learning, mobile learning, and smart learning. Conclusion - Since the research so far has mainly focused on online class management methods. Therefore, future research suggests that efforts should be made to develop educational contents and teaching methods using specific ICT technologies. These efforts will contribute to advancing smart education that future education aims for.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.1-6
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• 2023

Text mining (TM) is most widely used to process the various unstructured text documents and process the data present in the various domains. The other name for text mining is text classification. This domain is most popular in many domains such as movie reviews, product reviews on various E-commerce websites, sentiment analysis, topic modeling and cyber bullying on social media messages. Cyber-bullying is the type of abusing someone with the insulting language. Personal abusing, sexual harassment, other types of abusing come under cyber-bullying. Several existing systems are developed to detect the bullying words based on their situation in the social networking sites (SNS). SNS becomes platform for bully someone. In this paper, An Enhanced text mining approach is developed by using Ensemble Algorithm (ETMA) to solve several problems in traditional algorithms and improve the accuracy, processing time and quality of the result. ETMA is the algorithm used to analyze the bullying text within the social networking sites (SNS) such as facebook, twitter etc. The ETMA is applied on synthetic dataset collected from various data a source which consists of 5k messages belongs to bullying and non-bullying. The performance is analyzed by showing Precision, Recall, F1-Score and Accuracy.

• 산업경영시스템학회지
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• 제46권2호
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• pp.22-31
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• 2023

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

• 한국의류학회지
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• 제47권6호
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• pp.1096-1112
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• 2023

Many entrepreneurs who launched online fashion shopping malls faced failure compared to those who achieved success. Recognizing the importance of research that reflects reality, this study explores entrepreneurs' experiences during the failure process of online fashion shopping malls. Two studies utilized YouTube videos documenting such online fashion shopping malls' failure. Study 1 employed text mining techniques, including high-frequency analysis and topic modeling, while Study 2 used a qualitative research method, specifically grounded theory. Study 1 identified the prominent experiences of operating online fashion shopping malls, while Study 2 provided a holistic perspective on the failure processes. The integrated findings from both studies highlight that entrepreneurs' passion for fashion motivates them to establish online fashion shopping malls, yet they encounter numerous challenges during the operational process. Insufficient business preparation and operational capabilities contribute to their failure to achieve financial goals. Despite efforts to boost sales and profit, entrepreneurs often close their businesses due to inadequate funds and waning motivation. The outcomes of this study can inform us about the operational challenges faced by online fashion shopping malls and offer valuable insights for developing new strategies to sustain and improve them.

• 한국산업정보학회논문지
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• 제28권5호
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• pp.89-108
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• 2023

본 연구는 공식발표 통계지표의 적시성 확보를 위해 기존 Nowcasting 방법론을 살펴보고 실시간 경기 현황 분석이 가능한 Real-time nowcasting 모형을 운용하기 위한 대안 데이터와 그 수집 체계를 점검한다. 공공영역과 민간영역에서 경기지표를 예측할 수 있는 고빈도 실시간 데이터를 탐색하고, 나아가 데이터의 수집, 가공, 모형화를 위한 클라우드 기반의 구축과정을 제안한다. 더불어 Real-time nowcasting 모형 추정 및 데이터 관리에 있어 고려해야 할 요소를 확인함으로써 적시성 및 안정성을 갖춘 공식 통계지표의 예측 프로세스를 제시한다.