• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.265-267
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• 2009

디지털 제품 구매자는 일반적으로 온라인을 통하여 제품 웹사이트에서 제품의 특징이나 사용 후기를 충분히 보고 제품을 구입한다. 그리고 제품을 구입 후에 사용자는 온라인에서 제품의 매뉴얼을 통하여 익힌다. 이러한 과정에서 본 연구자는 사용자들의 온라인상에서 제품 구매 전, 후에서 문제점을 발견하고 확실히 어떠한 요구사항이 존재할 것이라는 의문을 가졌다. 따라서 다양한 일반인 연령대를 구성한 340명을 대상으로 디지털 제품의 전달 매체 사용에 대한 온라인 설문조사를 실시하였다. 설문문항 내용에서 우선적 대안으로 예상한 것들은 '데스크탑 가상현실'과 '멀티미디어 동영상' 매체들이다. 그리고 사용자들이 이것들을 새로운 매체로서 가장 요구하는 사항일 것이라는 추측을 하고 현황을 파악한다. 최종적으로는 사용자가 요구하는 제품에서 매체의 기능과 만족도를 조사하고 개선점을 파악하며 문제점에 대한 해결 대안의 기초 자료로서 제시하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.306-308
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• 2018

본 연구에서는 체중 감량을 위해 무분별한 다이어트 식품의 남용을 막고, 다이어트 보조 식품에 대한 정보를 제공하기 위해서 감성 분석을 활용하여 다이어트 보조 식품에 대한 온라인 후기를 분석하였다. 먼저, 다이어트 보조 식품을 그 특성에 따라 네 가지 종류로 분류하고 각 카테고리 별로 긍정 및 부정 점수를 계산하였다. 이를 위해 체중 감량에 대한 감성 사전을 다이어트 식품에 대한 후기를 텍스트 마이닝하여 구축하였다. 특히 부작용이 있는 식품에 대한 부정 점수에 가중치를 두기 위해서 WHO-ART 에서 정의한 부작용 용어에는 가중치를 두어 처리하였다. 분석 결과 단백질 보충 식품군이 긍정 점수가 가장 높게 나타났고, 이는 다이어트를 위한 목적 이외에도 운동을 전문적으로 하는 사람들에게 오랜기간 사용되어 왔기 때문인 것으로 해석된다. 또한 식욕 억제제 식품군이 긍정점수는 가장 낮고 부정 점수는 가장 높게 나타났는데, 이는 식욕억제제의 주성분인 펜타민에 의한 가능성이 클 것이라고 예측된다.

• Fashion & Textile Research Journal
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• v.19 no.2
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• pp.180-193
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• 2017

The increase use of smartphones has paved the way for quick dissemination of online information. This has a huge influence on consumers' purchase decision making and the formation of a company's image. As such, this study focuses on product review from mobile beauty applications(apps); in particular, the perceived information authenticity. The purpose is as follows. First, to examine if there is any difference in perceived information authenticity based on the types of product review. Second, to analyze how perceived information authenticity influences brand attitude, purchase intention, and electronic word of mouth(e-WOM) intention. The study targets consumers in their 20s and 30s who have experience buying a product via a mobile beauty app. Three hundred responses are analyzed using SPSS 21.0 and AMOS 18.0. The results reveal that, first of all, consumers derive higher perceived information authenticity from a multi-facet review rather than a double-facet review. Second, among the traits of perceived information authenticity, only a brand's perceived reliability has a significant influence on brand attitude. Third, this brand attitude has a positive influence on purchase intention and e-WOM intention. In conclusion, these findings can serve as an important discussion point for companies developing a mobile beauty app, drawing attention to perceived information authenticity, based on the types of product review.

• Journal of Fashion Business
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• v.14 no.5
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• pp.49-65
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• 2010

The purpose of this paper is to derive the categories of negative e-WOM (electronic word of mouth) via consumer review. Disclosing the details of negative e-WOM based consumer reviews has never been done before. For this reason, a content analysis was adopted to provide knowledge and understanding of the phenomenon. This paper analyzes the content of 630 consumer reviews posted on the open market internet site, www.auction.co.kr. The analysis was conducted from October 20th, 2008 to March 10th, 2009. The results indicated that the negative e-WOM based consumer reviews can be divided into two categories: the cognitive evaluation and the expression of consumer's emotion. The category of cognitive evaluation is consisted of negative e-WOM of product, negative e-WOM of service, and warning about the risk of purchasing products. The category of expressing consumers' emotion are composed of venting customers' dissatisfaction and passive response of dissatisfaction. Investigating the details of negative e-WOM has a number of implications. Most importantly, the results revealed multidimensional structure of negative e-WOM. This understanding of negative e-WOM communication allows marketers to improve products and services that better meet customers' current and future needs.

• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.

• 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.

• Proceedings of the KAIS Fall Conference
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• 2012.05a
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• pp.304-306
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• 2012

최근 들어 온라인 쇼핑몰과 SNS(Social Network Service)가 결합한 새로운 쇼핑방식인 소셜쇼핑(social shopping)과 소셜커머스(social commerce)가 핫이슈가 되었다. 소셜커머스는 이전에 존재하지 않던 수요를 창출해 낼 수 있다는 점에서 공동구매와는 전혀 다른 새로운 상거래방식이다. 소셜커머스는 50% 이상의 가격할인율과 특정 거래조건(품목, 거래가능 시간, 사용기간, 물량 등)의 제약을 통해 관심의 경제(economy of attention)를 극대화한 수익모델이라 할 수 있다. 본 연구에서는 소셜커머스에서 가격할인과 소비자들의 제품에 대한 지각된 위험은 충동구매에 어떤 영향을 미치는지에 대해서 검토하고자 하였다.

• 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.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.113-125
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• 2017

Customers have been affected by others' opinions when they make a purchase. Thanks to the development of technologies, people are sharing their experiences such as reviews or ratings through online or social network services, However, although ratings are intuitive information for others, many reviews include only texts without ratings. Also, because of huge amount of reviews, customers and companies can't read all of them so they are hard to evaluate to a product without ratings. Therefore, in this study, we propose a methodology to predict ratings based on reviews for a product. In a methodology, we first estimate the topic-review matrix using the Latent Dirichlet Allocation technic which is widely used in topic modeling. Next, we predict ratings based on the topic-review matrix using the artificial neural network model which is based on the backpropagation algorithm. Through experiments with actual reviews, we find that our methodology can predict ratings based on customers' reviews. And our methodology performs better with reviews which include certain opinions. As a result, our study can be used for customers and companies that want to know exactly a product with ratings. Moreover, we hope that our study leads to the implementation of future studies that combine machine learning and topic modeling.

• Economic Analysis
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• v.25 no.2
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• pp.54-95
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• 2019

The purpose of this study is to analyze quantitatively and qualitatively the effects of the increase in online shopping and its effects on real-world commercial outlets. The empirical analysis of this study is based on the results of "Census on Establishments" and "Online Shopping Survey" that cover 15 years, from 2002 to 2016. According to the results of this study, the increase in the number of online transactions affects the decrease in the number of stores in the real-world retail sector. However, non-specialized large stores and chain convenience stores showed an increase in the number of stores. In addition, the number of F&B stores increased the most in line with the increase in online transactions. This is because the increase in online transactions and in internet users led to the use of more delivery applications and the introduction of popular places on blogs or through social media. Street-level rents for medium and large-sized locations increased. In other words, it is seen that the demand for differentiated real-world stores that provide a good user experience increases, even though online transactions also increase. These results suggest that real-world stores should provide good user experiences in their physical locations with a certain size and assortment of goods.