• 한국IT서비스학회지
• /
• 제14권4호
• /
• pp.159-169
• /
• 2015

Customer reviews are one of the important sources for purchase decision makings in online stores. Online stores have tried to provide useful reviews in product pages to customers. To assess the usefulness of customer reviews before other users have voted enough on the reviews, diverse aspects of reviews were utilized in prevous studies. Style and semantic information were utilized in many studies. This study aims to test diverse alogrithms and datasets for identifying a proper classification method and threshold to classify useful reviews. In particular, most researches utilized ratio type helpfulness index as Amazon.com used. However, there is another type of usefulness index utilized in TripAdviser.com or Yelp.com, count type helpfulness index. There was no proper threshold to classify useful reviews yet for count type helpfulness index. This study used reivews and their usefulness votes on restaurnats from Yelp.com to devise diverse datasets and applied text mining approaches to classify useful reviews. Random Forest, SVM, and GLMNET showed the greater values of accuracy than other approaches.

• 한국전자거래학회지
• /
• 제16권3호
• /
• pp.93-112
• /
• 2011

최근 온라인 쇼핑 활동의 증가와 함께 소비자들은 온라인상에서의 제품에 대한 리뷰를 합리적인 구매 결정을 내리기 위한 중요한 정보로 활용하고 있다. 하지만 소비자들은 많은 양의 온라인 리뷰 중 그들의 구매 결정에 유익하게 활용될 리뷰를 선택하기가 쉽지 않다. 따라서 본 연구에서는 정교화 가능성 이론(elaboration likelihood model)을 바탕으로, 유익한 온라인 소비자 리뷰를 결정하는 요인이 무엇인지 알아보고, 구매하고자 하는 제품의 가격에 따라 유익한 리뷰를 결정짓는 요인이 어떻게 변화되는지를 분석하고자 한다. 본 분석을 위해 아마존 닷컴의 75,226개의 온라인 소비자 리뷰 데이터를 수집하고, 리뷰 메시지의 감정어 분석 (sentimental analysis)을 통해 메시지 내용에 대한 정량변수도 확보하였다. 다중회귀분석 결과, 리뷰 점수, 리뷰어에 대한 랭킹 정보를 포함하는 주변적 단서(peripheral cues)와 리뷰 메시지의 단어 수, 부정어 비율의 중심적 단서(central cues) 모두 리뷰의 유익성에 영향을 미치는 것으로 나타났다. 또한, 고가격 제품과 저가격 제품에서 유익한 리뷰를 결정하는 요인이 다르게 나타남을 확인하였다.

• 한국전자거래학회지
• /
• 제15권1호
• /
• pp.17-37
• /
• 2010

최근 온라인 상품검색기능을 제공하는 여러 사이트에서는 단순하게 특정상품을 검색하거나 신제품에 대한 소개 및 검색 기능뿐만 아니라 가격별 정렬, 제조사별 정렬, 출시일별 정렬 기능을 통해 상품을 판매하는 인터넷 쇼핑몰 사이트로의 연결기능과 함께 상품에 대한 주요 기능설명과 이용자들의 구매후기 등과 같은 다양한 서비스를 제공하고 있어, 온라인 쇼핑을 즐기는 소비자들에게 구매를 위한 준거점으로서 양질의 정보를 제공하고 있다. 본 연구는 온라인 상품 검색사이트에서 제공하는 이용후기의 역할을 살펴보고자 수행되었다. 이를 위해 온라인 상품검색사이트의 이용후기 특성으로 정보제공성과 유용성을 각각 설정하고, 온라인 상품검색사이트의 친숙성과 함께 사이트 신뢰, 만족, 충성도에 미치는 영향력 과정을 확인하기 위해 8개의 연구가설을 설정하였으며, 175명으로부터 자료를 수집, 이를 구조방정식 모형을 이용하여 검증하였다. 그 결과 친숙성과 이용후기의 정보제공성, 유용성 등이 사이트 신뢰, 만족을 경유하여 충성도에 유의적인 영향력을 미치고 있음을 확인하였다. 이를 통해 온라인 상품검색사이트 이용자의 충성도를 높이기 위해서는 사이트 친숙성을 높이는 전략, 제공되는 이용후기의 정보제공성을 높이고 유용성을 높이기 위한 운영전략의 마련이 필요하다 하겠다. 본 연구의 이러한 결과들이 향후 온라인 상품검색사이트를 운영하는 기업에게 많은 도움이 되었으면 한다.

• 한국정보과학회논문지:소프트웨어및응용
• /
• 제35권6호
• /
• pp.392-403
• /
• 2008

사용자가 작성한 리뷰는 다양한 활용성을 갖는 가치 있는 데이타이다. 특히 온라인 쇼핑몰에서의 상품평은 사용자의 구매 결정에 직접적인 영향을 미치는 중요한 정보이다. 본 논문에서는 실제 쇼핑몰 사이트에 있는 상품평을 분석하여 각 상품의 특징과 이에 대한 사용자의 의견을 요약하고 상품의 순위를 산정하는 상품평 분석 시스템을 설계하고 구현하였다. 상품평을 분석하는 과정에서는 자연언어처리 기법과 의미 사전을 사용한다. 의미 사전에는 상품의 특징을 표현하는 어휘와 각 어휘들의 극성(Polarity) 정보들을 반자동화된 도구들을 활용하여 정의할 수 있도록 구현하였다. 이에 더하여 문맥에 따라 다른 의미를 갖는 어휘를 의미 사전에서 정의하고 활용하는 방법에 대해서도 논의하였다. 실험은 2개 상품 분류의 20개 상품, 1796개의 실제 상품평을 수집하여 상품의 순위를 측정하고 주요 요소를 분석하는 방식으로 진행하였다. 그 중 2개 상품에 대한 63개의 상품평에 대하여 분석의 정확률과 재현율을 측정하였으며, 평균 88.94%의 정확률, 47.92%의 재현율을 나타내었다.

• 한국의류학회지
• /
• 제40권6호
• /
• pp.1010-1024
• /
• 2016

This study identifies the influence of on/off-line acquaintances' recommendation messages on fashion product attitude and purchase intention on the online purchase of fashion products in two-sided word of mouth situations as well as compares the difference in influence according to bond-base with equidistance. This study was conducted for one month on university students in their 20s who were believed to be active in smartphone use. Out of the collected 174 copies of the questionnaire, 162 copies were used for analysis. The questionnaire was classified into online and offline recommendation messages of an acquaintance. We present two-sided fashion product reviews made similar to the type found in an actual shopping mall web-site. As for analysis, confirmatory factory analysis, structural equation modeling, and multi-group analysis were conducted using AMOS 19.0. The analysis results are as follows. First, on/off-line acquaintances' recommendation messages had significant influences on product attitude in the situation where two-sided reviews on fashion products were presented; however, those messages did not influence purchase intention. Recommendation messages positively increased product attitude and enhanced purchase intention if acquaintances' recommendation messages were mediated between on/off-line acquaintances' recommendation messages and purchase intention. Consequently, a mediating effect on product attitude was revealed. Second, there was no difference between online acquaintances and offline acquaintances in terms of the influence of acquaintances' recommendation messages on product attitude and purchase intention, in the situation where two-sided reviews were presented on online fashion products. Therefore, no control effect according to the type of acquaintance was confirmed.

• 유통과학연구
• /
• 제18권10호
• /
• pp.65-75
• /
• 2020

Purpose: This study aims to examine how to review contents of experiential and utilitarian products (e.g., skincare products) and how to affect review helpfulness by applying natural language processing techniques. Research design, data, and methodology: This study uses 69,633 online reviews generated for the products registered at Amazon.com by 13 Korean cosmetic firms. The authors identify key topics that emerge about consumers' use of skincare products such as skin type and skin trouble, by applying bigram analysis. The review content variables are included in the review helpfulness model, including other important determinants. Results: The estimation results support the positive effect of review extremity and content on the helpfulness. In particular, the reviewer's skin type information was recognized as highly useful when presented together as a basis for high-rated reviews. Moreover, the content related to skin issues positively affects review helpfulness. Conclusions: The positive relationship between extreme reviews and helpfulness of reviews challenges the findings from prior literature. This result implies that an in-depth study of the effect of product types on review helpfulness is needed. Furthermore, a positive effect of review content on helpfulness suggests that applying big data analytics can provide meaningful customer insights in the online retail industry.

• Journal of Information Processing Systems
• /
• 제16권3호
• /
• pp.718-732
• /
• 2020

Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.

• 디지털산업정보학회논문지
• /
• 제19권1호
• /
• pp.91-107
• /
• 2023

With the recent expansion of the commerce ecosystem, a large number of user evaluations have been produced. Accordingly, attempts to create business insights using user evaluation data have been actively made. However, since user evaluation can change after the user experiences the product, it is difficult to say that the analysis based only on reviews immediately after purchase fully reflects the user's evaluation of the product. Moreover, studies conducted so far on user evaluation have overlooked the fact that the length of time a user has used a product can affect the user's product evaluation. Therefore, in this study, we build a model that predicts the direction of change in the user's rating after use from the user's rating and reviews immediately after purchase. In particular, the proposed model reflects the product's period of use in predicting the change direction of the star rating. However, since the posterior information on the duration of product use cannot be used as input in the inference process, we propose a structure that utilizes information about the product's period of use using an auxiliary classifier. As a result of an experiment using 599,889 user evaluation data collected from the shopping platform 'N' company, we confirmed that the proposed model performed better than the existing model in terms of accuracy.

• International journal of advanced smart convergence
• /
• 제13권2호
• /
• pp.44-47
• /
• 2024

With the advancement of Internet technology, online sales and purchases of products have become active. Along with this, the importance of user reviews is also being highlighted. Although user reviews are actively utilized for product sales and purchases, it is difficult to quickly and easily obtain useful information due to the abundance of user reviews. Therefore, prioritizing user reviews is a necessary service for customers that requires careful consideration. Metadata, which contains important information, can be effectively used to prioritize user reviews. However, it is crucial to select and use metadata appropriately according to the purpose. Lean Startup proposes a strategy of repeatedly correcting the problems of ideas or making early transitions to continue trying different approaches. In this paper, we propose a three-step method applying the Lean Startup process to analyze ways to prioritize user reviews using metadata: Build Priority, Measure Priority, Learn Priority.

• 지식경영연구
• /
• 제21권1호
• /
• pp.197-210
• /
• 2020

The smart speaker includes an AI assistant function in the existing portable speaker, which enables a person to give various commands using a voice and provides various offline services associated with control of a connected device. The speed of domestic distribution is also increasing, and the functions and linked services available through smart speakers are expanding to shopping and food orders. Through text mining-based customer review analysis, there have been many proposals for identifying the impact on customer attitudes, sentiment analysis, and product evaluation of product functions and attributes. Emotional investigation has been performed by extracting words corresponding to characteristics or features from product reviews and analyzing the impact on assessment. After obtaining the topic from the review, the effect on the evaluation was analyzed. And the market competition of similar products was visualized. Also, a study was conducted to analyze the reviews of smart speaker users through text mining and to identify the main attributes, emotional sensitivity analysis, and the effects of artificial intelligence attributes on product satisfaction. The purpose of this study is to collect blog posts about the user's experiences of smart speakers released in Korea and to analyze the attitudes of customers according to their attributes. Through this, customers' attitudes can be identified and visualized by each smart speaker product, and the positioning map of the product was derived based on customer recognition of smart speaker products by collecting the information identified by each property.