• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.117-119
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• 2016

Various sites which provide big data analysis service do not show the interpretation of analysis results such as social trends and events but simple numeric results. In this point, this study attempts to suggest a way of interpretation on big data analysis results focusing on brand awareness and preference.

• Journal of the Korean Society of Costume
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• v.63 no.5
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• pp.61-71
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• 2013

As an emerging e-retail model, private shopping malls are transforming the traditional retail systems. These malls are expanding in Europe and the United States and have recently arrived in Korea. This study investigates the relationships among the usefulness of SNS(Social Networking Sites), shopping values, and purchase intentions for fashion products in private shopping malls. The analysis was based on a survey of consumers, aged between 20 and 39, who recently purchased fashion products from a private shopping mall. Two hundred samples were used in the final analysis. The study employed descriptive statistics, Cronbach's alpha, confirmatory factor analysis, and structural equation modeling. The relationships among usefulness of SNS, shopping values, and purchase intentions was verified through structural equation modeling. More specifically, the usefulness of SNS significantly influenced the utilitarian shopping values as well as purchase intention of fashion products. Utilitarian shopping values, in turn, significantly influenced hedonic shopping values and purchase intention. Further, hedonic shopping values had a significant effect on purchase intention.

• Information Systems Review
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• v.16 no.1
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• pp.1-15
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• 2014

Despite the sizable growth of the group buying market, consumer complaints have recently raised skepticism about the future of these sites. Thus, building a trustworthy transaction environment has become a critical issue. In exploring a trust-building mechanism, we pay particular attention to the role of merchandisers who specialize in finding products or services and marketing them to potential buyers on group buying sites. The purpose of this study is to examine whether providing merchandiser feedback comments and performance profiles on group buying sites leads consumers to evaluate the community of merchandisers more favorably and makes them more likely to purchase products or services. Research hypotheses were tested with data obtained from 124 subjects who participated in a laboratory experiment. The results empirically demonstrate that merchandiser feedback comments and performance profiles enhance buyers' trust in the community of merchandisers participating in a group buying site. This enhanced trust, in turn, increased buyers' intention to purchase products or services through the group buying site.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.40 no.3
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• pp.46-56
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• 2022

This study aimed to analyze the locational characteristics of heritage sites in Seoul in order to identify flood susceptibility by type. As for the location factors related to flood susceptibility, elevation, slope, distance to streams, and topographic location were analyzed. Literature review was supplemented for the historical and humanistic environments of heritage sites. The results of the study are as follows. First, heritage sites in Seoul are distributed throughout the city, and are especially highly dense in the Hanyangdoseong fortress. It was also confirmed that heritage sites were concentrated around Jung-gu, Jongno-gu, Jingwan-dong, and Ui-dong in the quantitative spatial analyses. Second, types of heritage sites at the circumstance susceptible to flood damage were related to commerce and distribution, traffic, modern traffic and communication, geological monument, residence, government office, and palace. Third, heritage types with locational characteristics that showed low flood susceptibility were found to be natural scenic spots, telecommunication, ceramics, Buddhism, tombs, and tomb sculptural heritage assets. In a time when risk factors that can damage the value of heritage are gradually increasing due to anthropogenic influences along with changes in the natural environment, this study provides basic data for vulnerability analysis that reflects the unique characteristics of heritage assets. The results can contribute to more comprehensive and comprehensive insights for the management and protection of heritage by including the humanities and social science data together with natural factors in the analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.715-721
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• 2018

With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.652-657
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• 2018

With the rapid development of e-commerce, many customers can now express their opinion on various kinds of product at discussion groups, merchant sites, social networks, etc. Discerning a consensus opinion about a product sold online is difficult due to more and more reviews become available on the internet. Opinion Mining, also known as Sentiment analysis, is the task of automatically detecting and understanding the sentimental expressions about a product from customer textual reviews. Recently, researchers have proposed various approaches for evaluation in sentiment mining by applying several techniques for document, sentence and aspect level. Aspect-based sentiment analysis is getting widely interesting of researchers; however, more complex algorithms are needed to address this issue precisely with larger corpora. This paper introduces an approach of knowledge representation for the task of analyzing product aspect rating. We focus on how to form the nature of sentiment representation from textual opinion by utilizing the representation learning methods which include word embedding and compositional vector models. Our experiment is performed on a dataset of reviews from electronic domain and the obtained result show that the proposed system achieved outstanding methods in previous studies.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.335-346
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• 2015

The electronic commerce site (EC site) has become an important marketing channel where consumers can purchase many kinds of products; their access logs, including purchase records and browsing histories, are saved in the EC sites' databases. These log data can be utilized for the purpose of web marketing. The customers who purchase many product items are good customers, whereas the other customers, who do not purchase many items, must not be good customers even if they browse many items. If the attributes of good customers and those of other customers are clarified, such information is valuable as input for making a new marketing strategy. Regarding the product items, the characteristics of good items that are bought by many users are valuable information. It is necessary to construct a method to efficiently analyze such characteristics. This paper proposes a new latent class model to analyze both purchasing and browsing histories to make latent item and user clusters. By applying the proposal, an example of data analysis on an EC site is demonstrated. Through the clusters obtained by the proposed latent class model and the classification rule by the decision tree model, new findings are extracted from the data of purchasing and browsing histories.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.123-131
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• 2020

After observing that most tourists plan to complete their visits to multiple cultural heritage sites within one day, we surmised that for many museum visitors, the foremost thought is with regard to the amount of time is to be spent at each location and how they can maximize their enjoyment at a site while still balancing their travel itinerary? Recommendation systems in e-commerce are built on knowledge about the users' previous purchasing history; recommendation systems for museums, on the other hand, do not have an equivalent data source available. Recent solutions have incorporated advanced technologies such as algorithms that rely on social filtering, which builds recommendations from the nearest identified similar user. Our paper proposes a different approach, and involves providing dynamic recommendations that deploy social filtering as well as content-based filtering using term frequency-inverse document frequency. The main challenge is to overcome a cold start, whereby no information is available on new users entering the system, and thus there is no strong background information for generating the recommendation. In these cases, our solution deploys statistical methods to create a recommendation, which can then be used to gather data for future iterations. We are currently running a pilot test at Chao Samphraya national museum and have received positive feedback to date on the implementation.

• The Journal of Korean Medical History
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• v.28 no.2
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• pp.1-13
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• 2015

Purpose : Local medicinal herb festivals present the history of the Korean medicine and cultural resources in the regions to play an important role in attracting tourists, vitalizing local economy, and improving the local image. Therefore, it is important to understand the origin of the festival and grasp historical and cultural meaning of local medicinal herb festivals. Methods : I compared the books and articles presented in the reference list. Results & Conclusions : Local medicinal herb festivals originates from traditional yangnyeongsi. Since the $17^{th}$ century, yangnyeongsi has grown up as a special market. Implementation of the Daedong Act promoted commodity and monetary economy that helped commerce and industry develop and market grow up. It also made changes in the social status system and yangnyeongsi has been such a historical phenomenon appeared in this social background. The growth of yangnyeongsi contributed to the progress in the private medicine that triggered the gradual transfer of power in the medicine to the private sector which has long been held by the government. In yangnyeongsi, there were many cultural events to attract visitors. It's the same case in China that preserves stages that were used for cultural events in the medicine market to pass down the historic sites while those in Korea are disappearing as yangnyeongsis are being pulled out of the city areas to the suburban areas due to the redevelopment projects. For this reason, restoration of the place for traditional yangnyeongsi should be taken into account through local medicinal herb festivals.