• Journal of Information Technology Services
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• v.18 no.1
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• pp.153-171
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• 2019

Competition among cities has become fierce with decentralization and globalization, and each city tries to establish a brand image of the city to build its competitiveness and implement its policies based on it. At this time, surveys, expert interviews, etc. are commonly used to establish city brands. These methods are difficult to establish as sampling methods an empirical component, the biggest component of a city brand. In this paper, therefore, based on the precedent research's urban brand measurement and components, the words representing each city image property were extracted and relocated to five indicators to form the evaluation index. The constructed indicators have been validated through the review of three experts. Through the index, we analyzed the brands of four cities, Ulsan, Incheon, Yeosu, and Gyeongju, and identified the factors by using Topic Modeling and Word Cloud. This methodology is expected to reduce costs and monitor timely in identifying and analyzing urban brand images in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Review of KIISC
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• v.7 no.4
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• pp.73-79
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• 1997

웨어하우스나 다른 데이타베이스에 있는 데이터를 어떤 유용한 정보로 변환하는 기술은 데이터 탐사이다. 즉, 데이터 탐사는 데이터베이스의 많은 데이터에서 이전에는 몰랐던 정보를 추출하기 위해 일련의 적당한 질의들을 취하는 과정이다. 데이타 탐사 기술은 통계, 기계 이해(machine learning), 데이타베이스 관리, 병렬처리 (preallel processing)등을 포함한 다양한 기술들의 혼합이다. 본 연구에서는 데이터 탐사에서 기인될 보안 위협, 이런 위협을 처리하기 위한 기법, 보안 문제점을 처리할 도구로서 데이터 탐사의 이용 등을 알아볼 것이다.

• Korea Information Processing Society Review
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• v.8 no.6
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• pp.38-43
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• 2001

본 고에서는 인터넷 쇼핑몰 기업들 중 신생기업들을 대상으로 이들의 기업환경에 맞는 데이터베이스 마케팅 방법론을 제시하고자 한다. 그러므로 데이터마이닝(Data Mining)을 이용하여 기존고객을 세분화한 다음 고객 개개인의 특성에 맞는 마케팅을 프로모션(Promotion)하고 신규고객을 획득할 때는 신규고객의 특성을 미리 예측하여 고객의 평생가치(LTV:Life Value)를 촉진하여 기업과 고객과의 관계성을 높이고, 기업은 안정된 고객층으로부터 수익을 창출하고, 기업으로부터 더 많은 혜택을 받게 하는 것에 대하여 연구하였다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.214-215
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• 2022

This study analyzed the development stage and change management necessity of the construction IT system through existing research and literature review, and used WordCloud, one of the text mining techniques, to analyze current construction trends and major issues. The necessity of change management is derived by using existing research literature and construction-related social issues as analysis data.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.63-82
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• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.

• International Commerce and Information Review
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• v.7 no.2
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• pp.3-23
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• 2005

This paper object is application of electronic Customers Relationship Management(e-CRM) for buyer relationship commitment in korea export firms. So, I'd like to suggest some applications of e-CRM needed to strengthen the export firms in korea. These applications are as follows First, the export companies are required to e-CRM logical architecture that is needs to achievement of buyer relationship commitment. Second, Buyer data source is classify in to three large group by outside data, transaction data and support data. Third, a concept and function of buyer information database. Fourth, e-CRM campaign management for export marketing. Fifth, interaction of buyer and customizing. finally, a point to be considered of korea export companies are national character, data mining out of buyer information database, difference of data gathering and sustaining up date of buyer's new information.

• International Commerce and Information Review
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• v.7 no.3
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• pp.77-94
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• 2005

This paper object is application of electronic Customers Relationship Management(e-CRM) for buyer relationship commitment in korea export firms. So, I'd like to suggest some applications of e-CRM needed to strengthen the export firms in korea. These applications are as follows First, the export companies are required to e-CRM logical architecture that is needs to achievement of buyer relationship commitment. Second, Buyer data source is classify in to three large group by outside data, transaction data and support data. Third, a concept and function of buyer information database. Fourth, e-CRM campaign management for export marketing. Fifth, interaction of buyer and customizing. finally, a point to be considered of korea export companies are national character, data mining out of buyer information database, difference of data gathering and sustaining up date of buyer's new information.

• Tunnel and Underground Space
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• v.30 no.1
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• pp.29-38
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• 2020

Recent research trend of North Korean mining and rock engineering in the past 10 years was analyzed by a literature review of mining and rock engineering papers published in North Korean major mining journals, 'mining engineering', 'geological and geographical science' and 'technology innovation' published in 2008-2017. Basic database was established by organizing bibliographic information and abstract data of research papers in each journal. For each journal, paper submission trend classified by research field was analyzed using the basic database. And further study was conducted to the papers which showed distinguishing methodology and result, to analyze the trend of North Korean mining and rock engineering. The literature study showed a recent trend of quantification and automation in mining and rock engineering researches in North Korea, which seems due to recent changes in North Korea's science and technology policy and deterioration of the mining conditions. The results of this study can be applied in the feasibility studies of North Korea's mineral resource development projects. Future inter-Korean technical cooperation and site survey on North Korean field can secure complement the reliability of this study.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.435-460
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• 2013

Recommender systems are popular applications that help users to identify items that they could be interested in. A recent research area on recommender systems focuses on detecting several kinds of inconsistencies associated with the user preferences. However, the majority of previous works in this direction just process anomalies that are intentionally introduced by users. In contrast, this paper is centered on finding the way to remove non-malicious anomalies, specifically in collaborative filtering systems. A review of the state-of-the-art in this field shows that no previous work has been carried out for recommendation systems and general data mining scenarios, to exactly perform this preprocessing task. More specifically, in this paper we propose a method that is based on the extraction of knowledge from the dataset in the form of rating regularities (similar to frequent patterns), and their use in order to remove anomalous preferences provided by users. Experiments show that the application of the procedure as a preprocessing step improves the performance of a data-mining task associated with the recommendation and also effectively detects the anomalous preferences.