• Journal of Information Technology Services
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• v.17 no.2
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• Distribution Business Review
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• no.3
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• pp.121-139
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• 2003

최근 내수경기의 불황에도 불구하고 지속적인 고성장세를 보이고 있는 TV홈쇼핑 시장에 대한 산업 전체적인 측면에서 경쟁요인을 분석하였다. 그 결과로써 정부와 업계에 향후 TV 홈쇼핑 시장의 발전 방향을 제시하고 국내 유통시장의 발전추세와 구조적 특성을 파악함으로써 정책적인 제언을 하고자 한다. 이를 위해 TV홈쇼핑 시장의 경쟁요인을 외부경쟁요인과 내부경쟁요인으로 분류하였다. 구체적으로 외부경쟁요인은 업태 특징에 의한 경쟁요인과 국내ㆍ외적 유통산업 변화추세에 따른 경쟁요인으로, 내부경쟁요인은 CATV 시청자구수의 변화, 신규사업자 진출, 시장집중도, 수익률 향상 전략, SO채널확보 및 투자 인터넷쇼핑몰 운영 등으로 분류하여 분석하였다. 이러한 TV 홈쇼핑 시장의 내부ㆍ외부경쟁요인의 도출을 통해 업계에는 효율적인 전략의 일환으로 인터넷쇼핑몰의 매출비중확대, M-Commerce와 B2B 전자상거래 부분으로의 사업확대, 온라인과 오프라인의 연계방안 강구를 통한 틈새시장개발, 고객정보 확보를 통한 CRM, SCM 활용 및 개발 등을 제시하고, 정부에는 업체간 경쟁유발을 통한 소비자의 편익증대, 경쟁확대로 인한 소비자 피해최소화, 유통 인프라에 대한 투자확대 및 제도 보완 등을 정책방향으로 제시하고자 한다.

• Knowledge Management Research
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• v.22 no.1
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• pp.269-286
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• 2021

As the un-tact and on-tact consumption culture has proliferated due to the impact of COVID-19, 'live commerce', a form of shopping while communicating with customers through real-time streaming broadcasting, is emerging in the commerce and distribution industry. Live commerce provides an environment where customers can get the convenience of online shopping and enjoy un-tact shopping more realistically while communicating with the broadcaster in real time, as if purchasing directly from an offline store. Therefore, purchases using live commerce are expected to increase further. In this study, based on the characteristics of live commerce, the main factors influencing the increase in purchase intention through live commerce were derived and their influences were verified. In particular, this study examined these factors in multiple dimensions with focusing on strong interactivity, realistic presence, and providing detailed reviews with high credibility for products as the features of live commerce. This research collected sample data from actual users of live commerce and empirically analyzed the significance of the factors influencing the purchase increase of live commerce, thereby providing implications for knowledge management in a newly changed commerce environment in the un-tact era.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.970-977
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• 2020

The collaborative filtering recommendation technique has been the most widely used since the beginning of e-commerce companies introducing the recommendation system. As the online purchase of products or contents became an ordinary thing, however, recommendation simply applying purchasers' ratings led to the problem of low accuracy in recommendation. To improve the accuracy of recommendation, in this paper suggests the method of collaborative filtering that analyses product reviews and uses them as a weighted value. The proposed method refines product reviews with text mining to extract features and conducts sentiment analysis to draw a sentiment score. In order to recommend better items to user, sentiment weight is used to calculate the predicted values. The experiment results show that higher accuracy can be gained in the proposed method than the traditional collaborative filtering.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.160-172
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• 2023

The metaverse, derived from the fusion of "meta" and "universe," encompasses a three-dimensional virtual realm where avatars actively participate in a range of political, economic, social, and cultural activities. With the recent development of the metaverse, the traditional way of experiencing services is changing. While existing studies have mainly focused on the technological advancements of metaverse services (e.g., scope of technological enablers, application areas of technologies), recent studies are focusing on evaluating the quality of experience (QoE) of metaverse services from a customer perspective. This is because understanding and analyzing service characteristics that determine QoE from a customer perspective is essential for designing successful metaverse services. However, relatively few studies have explored the customer-oriented approach for QoE evaluation thus far. This study conducted an online review analysis using text mining to overcome this limitation. In particular, this study analyzed 227,332 online reviews of the Roblox service, known as a representative metaverse service, and identified points for improving the Roblox service based on the analysis results. As a result of the study, nine service features that can be used for QoE evaluation of metaverse services were derived, and the importance of each feature was estimated through relationship analysis with service satisfaction. The importance estimation results identified the "co-experience" feature as the most important. These findings provide valuable insights and implications for service companies to identify their strengths and weaknesses, and provide useful insights to gain an advantage in the changing metaverse service environment.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.493-498
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• 2024

With the rapid advancement of generative artificial intelligence technology, there is growing interest in how to utilize it in practical applications. Additionally, the importance of prompt engineering to generate results that meet user demands is being newly highlighted. Exploring the new possibilities of generative AI can hold significant value. This study aims to utilize ChatGPT 4.0, a leading generative AI, to propose an effective method for evaluating user experience through the analysis of online customer review data. The user experience evaluation method was based on the six-layer elements of user experience: 'functionality', 'reliability', 'usability', 'convenience', 'emotion', and 'significance'. For this study, a literature review was conducted to enhance the understanding of prompt engineering and to grasp the clear concept of the user experience hierarchy. Based on this, prompts were crafted, and experiments for the user experience evaluation method were carried out using the analysis of collected online customer review data. In this study, we reveal that when provided with accurate definitions and descriptions of the classification processes for user experience factors, ChatGPT demonstrated excellent performance in evaluating user experience. However, it was also found that due to time constraints, there were limitations in analyzing large volumes of data. By introducing and proposing a method to utilize ChatGPT 4.0 for user experience evaluation, we expect to contribute to the advancement of the UX field.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.115-130
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• 2011

Many online stores bring features that can build trust in their customers. More so, the number of products or content services on online stores has been increasing rapidly. Hence, personalization on online stores is considered to be an important technology to companies and customers. Recommender systems that provide favorable products and customer product reviews to users are the most commonly used features in this purpose. There are many studies to that investigated the relationship between social presence as an antecedent of trust and provision of recommender systems or customer product reviews. Many online stores have made efforts to increase perceived social presence of their customers through customer reviews, recommender systems, and analyzing associations among products. Primarily because social presence can increase customer trust or reuse intention for online stores. However, there were few studies that investigated the interactions between recommendation type, product type and provision of customer product reviews on social presence. Therefore, one of the purposes of this study is to identify the effects of personalized recommender systems and compare the role of customer reviews with product types. This study performed an experiment to see these interactions. Experimental web pages were developed with $2{\times}2$ factorial setting based on how to provide social presence to users with customer reviews and two product types such as hedonic and utilitarian. The hedonic type was a ringtone chosen from Nate.com while the utilitarian was a TOEIC study aid book selected from Yes24.com. To conduct the experiment, web based experiments were conducted for the participants who have been shopping on the online stores. Participants were a total of 240 and 30% of the participants had the chance of getting the presents. We found out that social presence increased for hedonic products when personalized recommendations were given compared to non.personalized recommendations. Although providing customer reviews for two product types did not significantly increase social presence, provision of customer product reviews for hedonic (ringtone) increased perceived social presence. Otherwise, provision of customer product reviews could not increase social presence when the systems recommend utilitarian products (TOEIC study.aid books). Therefore, it appears that the effects of increasing perceived social presence with customer reviews have a difference for product types. In short, the role of customer reviews could be different based on which product types were considered by customers when they are making a decision related to purchasing on the online stores. Additionally, there were no differences for increasing perceived social presence when providing customer reviews. Our participants might have focused on how recommendations had been provided and what products were recommended because our developed systems were providing recommendations after participants rating their preferences. Thus, the effects of customer reviews could appear more clearly if our participants had actual purchase opportunity for the recommendations. Personalized recommender systems can increase social presence of customers more than nonpersonalized recommender systems by using user preference. Online stores could find out how they can increase perceived social presence and satisfaction of their customers when customers want to find the proper products with recommender systems and customer reviews. In addition, the role of customer reviews of the personalized recommendations can be different based on types of the recommended products. Even if this study conducted two product types such as hedonic and utilitarian, the results revealed that customer reviews for hedonic increased social presence of customers more than customer reviews for utilitarian. Thus, online stores need to consider the role of providing customer reviews with highly personalized information based on their product types when they develop the personalized recommender systems.

• Distribution Business Review
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• no.3
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• pp.155-170
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• 2003

본 연구는 기본적으로 온라인과 오프라인의 통합마케팅을 절명하고 이에 관한 경험적 사례를 개발하기 위한 목적으로 진행되었다. 앨범서비스 영역의 공생적 기반 위에 전개되는 온 -오프라인의 경험적 사례를 개발하고 바람직한 마케팅방향 방향을 제시하였다. 본 연구는 문헌연구와 사례연구를 병행하여 연구를 진행하였다. 사례는 인터넷 기반의 앨범서비스를 제공하기 위한 (주)와이드스쿨이다. 온-오프라인의 협력적 통합마케팅의 전략을 전개하기 위해서는 무엇보다도 온 -오프라인의 뚜렷한 목표를 절정하고 성장방향에 대한 비전을 공유하고 나아가 온 -오프라인의 사명을 감당하는 것이다. 즉, 실천적으로는 \circled1 항상 고객 (customer)기반의 의사결정을 이루며 \circled2 철저한 협력적 돕는 경쟁(competition) 의식과 \circled3 구성원 자신의 일에 대한 자신감(confidence)을 지니고 \circled4 실천을 위한 용기(courage)를 가지고 \circled4 혁신하여 변화(change)를 선도하는 것이다. 온라인(on line)으로 표현되는 인터넷환경은 모든 영역에 변화를 요구하고 있다. 온라인에 관한 연구는 크게 온라인시장의 경쟁(competition)에 관한 연구와, 온라인 소비자(consumer)에 관한 연구 그리고 온라인 시장 참가기업(company)에 관한 연구로 구분된다(이석규 ; 2001). 이중 기업에 관한 연구의 중심에는 e-biz의 수익모텔에 관한 연구가 주류를 이루고 았다(David et al, 1999) 특히 오프라인기업의 경우 어떠한 형태방법으로 온라인 환경에 부응하며 기존의 마케팅활동과 연계할 것인가는 매우 중요한 문제다. 즉 기존의 오프라인기업이 온라인도구변화에 적응하고 이를 전략적으로 활용하기 위해서는 무엇보다도 오프라인과 온라인의 통합에 관한 형태와 전략 등을 명확히 이해하고 적용하는 것이 중요하다. 개수가 감소하는 것과는 상당히 다른 분포이다. 따라서 우리의 관측 결과는 2001년 사자자리 유성우의 극대 시간 전후 2시간에 적어도 0등급 이하의 밝은 유성이 상대적으로 많이 발생하였을 것으로 해석된다. 이런 밝은 유성의 빈도는 유성우 특성 연구에 중요한 의미를 가진다. 그러나 표준성만을 이용해 결정된 유성 등급은 유성의 지속 시간에 대한 불확실성과 전천 카메라 감응도의 비선형성에 의한 불확실성을 내포하고 있음을 지적해 둔다.umn chromatography)를 사용하였고 일련의 정제 과정을 통하여 배양액 중의 L-lactic acid 정제 수율은 약 85% 정도로 나타났으며 HPLC로 분석한 결과 99.7%의 순도를 확인할 수 있었다.경향을 나타내며 유입휫수와 $Dst_{min}$ 사이에는 높은 상관관계(0.83)가 있었다. 둘째, 주상기간 중 자기폭풍의 크기가 클수록 플럭스 비 ($f_{max}/f_{ave}$는 대체로 증가하는 경향을 나타냈다. 그리고 75~113keV 에너지 채널에서의 $Dst_{min}$ 값과 플럭스 비의 상관계수는 0.74로서 가장 높았으며 나머지 에너지 채널 역시 비교적 높은 상관관계를 나타냈다. 셋째, 주상기간 중 총 에너지 유입률 지수와 $Dst_{min}$ 사이에 높은 상관관계가 확인되었다. 특히 환전류를 구성하는 주요 입자의 에너지 영역(75~l13keV)에서 가장 높은(0.80) 상관계수를 기록했다. 넷째, 회복기 중에 일어나는 입자들의 유입은 자기폭풍의 지속시간을 연장시키는 경향을 보이며 큰 자기폭풍일수록 현저했다. 주상에서 관측된 이러한 특성은 서브스톰 확장기 활동이 자기폭풍의 발달과 밀접한 관계가 있음을 시사한다.se that were all low in two aspects, named "the Nonsignificant group".

• The KIPS Transactions:PartD
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• v.18D no.4
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• pp.237-244
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• 2011

Opinion Mining summarizes with classifying sensitive opinions of customers in huge online customer reviews for the attributes of products or services by positive and negative opinions. Because the customers represent their interests through subjective opinions as well as objective facts, the existing opinion mining techniques, which can analyze just the sensitive opinions, need to be expanded.. In this paper, We propose the novel entity association network model which expands the existing opinion mining techniques. The entity association model can not only represent positive and negative degree of the sensitive opinions, but also can represent the degree of the associations and relative importances between entities. We designed and implemented the customer reviews analysis system based on the entity association network model. We recognized that the system can represent more abundant information than the existing opinion mining techniques.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.265-274
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• 2021

In this paper, we propose a novel recommendation model based on Doc2vec using search keywords and product details. Until now, a lot of prior studies on recommender systems have proposed collaborative filtering (CF) as the main algorithm for recommendation, which uses only structured input data such as customers' purchase history or ratings. However, the use of unstructured data like online customer review in CF may lead to better recommendation. Under this background, we propose to use search keyword data and product detail information, which are seldom used in previous studies, for product recommendation. The proposed model makes recommendation by using CF which simultaneously considers ratings, search keywords and detailed information of the products purchased by customers. To extract quantitative patterns from these unstructured data, Doc2vec is applied. As a result of the experiment, the proposed model was found to outperform the conventional recommendation model. In addition, it was confirmed that search keywords and product details had a significant effect on recommendation. This study has academic significance in that it tries to apply the customers' online behavior information to the recommendation system and that it mitigates the cold start problem, which is one of the critical limitations of CF.