• 한국IT서비스학회지
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• 제15권1호
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• pp.97-111
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• 2016

In the creative economy paradigm, motivation of the opportunity based startup is a continuous concern to policy-makers. Recently, bigdata anlalytics challenge traditional methods by providing efficient ways to identify social trend and hidden issues in the public sector. In this study the authors introduce a case study using social bigdata analytics for conducting policy analysis. A semantic network analysis was employed using textual data from social media including online news, blog, and private bulletin board which create buzz on the startup business. Results indicates that each media has been forming different discourses regarding government's policy on the startup business. Furthermore, semantic network structures from private bulletin board reveal unexpected social burden that hiders opening a startup, which has not been found in the traditional survey nor experts interview. Based on these results, the authors found the feasibility of using social bigdata analysis for policy-making. Methodological and practical implications are discussed.

• 지식경영연구
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• 제23권3호
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• pp.285-311
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• 2022

본 연구에서는 S-O-R 프레임워크를 기반으로 개인에 대한 자극(유튜브 채널의 영상 특성, 유튜버 특성, 실시간 방송 특성)이 어떻게 유기체(지각된 유용성, 지각된 즐거움, 사회적 존재감)를 형성하고, 이것이 시청자 반응(지속시청의도, 유료후원의도)에 영향을 미치는지를 유튜브 실시간 방송 환경에서 검증해 보고자 한다. 이를 위해 연구모형 및 가설을 구성하였고, 유튜브 플랫폼의 실시간 방송 채널 서비스 이용자를 대상으로 수집한 369부의 설문자료를 분석하였다. 분석결과, 일부 영상 특성, 유튜버 특성, 실시간 방송 특성이 시청자의 지각된 유용성, 지각된 즐거움, 사회적 존재감에, 더 나아가 지속시청의도, 유료후원의도에 유의한 영향을 미치는 것을 확인하였다. 결론에서 연구결과의 이론적 및 실무적 시사점을 논의하였다.

• 디지털융복합연구
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• 제14권12호
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• pp.591-597
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• 2016

타당한 연구 수행을 위해서는 방법론이 중요하다. 소셜네트워크서비스로부터 수집되는 데이터를 대상으로 하는 소셜 빅데이터 연구는 최근 들어 새롭게 부각되는 연구이지만 아직 이에 합당한 연구방법이 충분하지 않은 실정이다. 이에 본 연구에서는 소셜 빅데이터 분석에 합당한 연구방법론 개발에 앞서, 연구문제의 설정에 대하여 체계적으로 정리하고 질문의 기본 유형을 제시하고자 한다. 그리고 제시되는 6가지 기본 유형에 따른 데이터 형태를 살펴보고자 한다. 나아가 SNS로부터 수집되는 빅데이터 분석과 관련된 통계적인 제 문제에 대해서도 고찰해 보도록 하겠다. 본 연구의 결과는 향후 관련 연구자들이 데이터 유형에 맞는 올바른 연구문제를 수립하고 분석함으로써 타당한 정보를 도출하는데 도움이 될 것으로 사료된다.

• Asia pacific journal of information systems
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• 제33권3호
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• pp.677-701
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• 2023

With the rapid advancement of social media analytics and artificial intelligence, many studies have used online customer reviews as an important source to measure service quality in many industries, including the hotel industry. However, these studies have failed to identify the relative importance of different dimensions of service quality and their role in customer satisfaction. To fill this research gap, this study aims to identify the effects of service quality on hotel customer satisfaction from the multidimensional perspectives using sentiment analysis with self-training on online reviews. Additionally, the moderating role of the brand of origin for each service quality dimension is also investigated. Drawing on the SERVQUAL model and brand of origin concept, this study develops 12 hypotheses and empirically tests them using 30,070 online customer hotel reviews collected from TripAdvisor.com. The results indicated that overall service quality and each dimension of SERVQUAL significantly influenced customer satisfaction of hotels. The results also confirmed the moderating effects of brand of origin on overall service quality. However, the moderating effects of brand of origin for the tangible, reliability, and empathy dimensions of service quality were significant, whereas the effects for responsiveness and assurance were not. This study sheds new light on service quality measurement by analyzing the multidimensional features of service quality and the role of brand of origin in the hotel service context.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제21권1호
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• pp.46-51
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• 2015

폭발적으로 증가하는 데이터와 급변하는 기술적 발전은 과거와 현재를 넘어 미래를 예견하고 대응할 수 있는 새로운 분석 패러다임을 요구한다. 지시적 분석은 목표를 설정하고 이를 달성하기 전략을 수립함으로써 분석 결과의 제시에 그치는 게 아니라 사용자에게 목표 달성을 위한 구체적 행동과 그 결과를 요구한다는 점에서 기존의 기술적 분석, 예측적 분석과 근본적인 차이점을 보여준다. 그렇지만, 아직까지 구체적인 구현 방안이 널리 연구되고 있지 않고 있다. 본 연구에서는 연구 역량 강화를 목적으로 개발되고 있는 InSciTe Advisory 사례를 통해 고려할 사항과 어떤 개발 요소들이 필요한 지를 살펴봄으로써 해당 연구 분야의 기반을 제시하고자 한다. InSciTe Advisory 시스템은 5W1H 방법론을 중심으로 연구자가 롤 모델 그룹에 도달하기 위한 전략을 수립할 수 있음을 보이며, 평가 모델을 통해 Elsevier SciVal과 비교하여 126.5%라는 비교 우위적 평가 결과를 얻었다.

• Nutrition Research and Practice
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• 제15권sup1호
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• pp.110-121
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• 2021

BACKGROUND/OBJECTIVES: Coronavirus disease 2019 (COVID-19) cases were first reported in December 2019, in China, and an increasing number of cases have since been detected all over the world. The purpose of this study was to collect significant news media reports on food services during the COVID-19 crisis and identify public communication and significant concerns regarding COVID-19 for suggesting future directions for the food industry and services. SUBJECTS/METHODS: News articles pertaining to food services were extracted from the home pages of major news media websites such as BBC, CNN, and Fox News between March 2020 and February 2021. The retrieved data was sorted and analyzed using Python software. RESULTS: The results of text analytics were presented in the format of the topic label and category for individual topics. The food and health category presented the effects of the COVID-19 pandemic on food and health, such as an increase in delivery services. The policy category was indicative of a change in government policy. The lifestyle change category addressed topics such as an increase in social media usage. CONCLUSIONS: This study is the first to analyze major news media (i.e., BBC, CNN, and Fox News) data related to food services in the context of the COVID-19 pandemic. Text analytics research on the food services domain revealed different categories such as food and health, policy, and lifestyle change. Therefore, this study contributes to the body of knowledge on food services research, through the use of text analytics to elicit findings from media sources.

• Journal of Smart Tourism
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• 제1권3호
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• pp.21-29
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• 2021

This study explored the relationship between post types and linguistic characteristics in marketer-generated content and social media engagement to find the optimized content to enhance social media engagement level. Post data of 23,588 marketer-generated content were collected from 50 states' destination marketing organization Facebook pages in the United States. The collected data were analyzed by employing social media analytics, linguistic analysis, multivariate analysis of variance, and discriminant analysis. The results showed that there are significant differences in both engagement indicators and linguistic scores among the three post types. Based on research findings, this research not only provided researchers with theoretical implications but also suggested practitioners the most effective content designs for travel destination marketing in Facebook.

• Journal of Information Processing Systems
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• 제17권1호
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• pp.163-177
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• 2021

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users' interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster's nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.358-368
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• 2022

Social media is a window for everyone, individuals, communities, and companies to spread ideas and promote trends and products. With these opportunities, challenges and problems related to security, privacy and rights arose. Also, the data accumulated from social media has become a fertile source for many analytics, inference, and experimentation with new technologies in the field of data science. In this chapter, emphasis will be given to methods of trend analysis, especially ensemble learning methods. Ensemble learning methods embrace the concept of cooperation between different learning methods rather than competition between them. Therefore, in this chapter, we will discuss the most important trends in ensemble learning and their applications in analysing social media data and anticipating the most important future trends.

• 농업과학연구
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• 제46권4호
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• pp.955-969
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• 2019

Analyzing on-line consumer responses is directly related to the management performance of food companies. Therefore, this study collected and analyzed data from an on-line portal site created by consumers about food companies with issues and examined the relationships between the data and the management performance. Through this process, we identified consumers' awareness of these companies obtained from big data analysis and analyzed the relationship between the results and the sales and stock prices of the companies through a time-series graph and correlation analysis. The results of this study were as follows. First, the result of the text mining analysis suggests that consumers respond more sensitively to negative issues than to positive issues. Second, the emotional analysis showed that companies' ethics issues (Enterprise 3 and 4) have a higher level of emotional continuity than that of food safety issues. It can be interpreted that the problem of ethical management has great influence on consumers' purchasing behavior. Finally, In the case of all negative food issues, the number of word frequency and emotional scores showed opposite trends. As a result of the correlation analysis, there was a correlation between word frequency and stock price in the case of all negative food issues and also between emotional scores and stock price. Recently, studies using big data analytics have been conducted in various fields. Therefore, based on this research, it is expected that studies using big data analytics will be done in the agricultural field.