• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.79-81
• /
• 2014

People increasingly rely on sources of information from E-commerce reviews. Product reviews is an important determinant of potential customers' buying choices. They are also utilized by product manufacturers to find problems of their products and to collect competitive intelligence information about their competitors. Unfortunately, it is well-known that many online product reviews are not made by genuine costumers of products. Reviewers could write some undeserving positive reviews to promote or fake negative reviews to defame some certain product, and we call them fake product reviews. Fake product review detection makes an attempt to detect fake reviews and removes them to restore the truthful ones for readers. To the best of our knowledge, there is still less published study on this problem. In this paper, we make a survey and an attempt to give a brief overview on fake product review detection. The related work of fake product review detection is presented including web spam and spam email. Then some methods to detect fake reviews are introduced and summarized. The trend of fake product review detection is concluded finally.

• The Journal of the Korea Contents Association
• /
• v.20 no.7
• /
• pp.316-330
• /
• 2020

The study analyzes reviews of hardware products, customer service products, and products that take the form of a convergence of hardware and cloud services in ICT using text mining. We derive keywords of each review and find the differentiation of words that are used to derive topics. A cluster analysis is performed to categorize reviews into their respective clusters. Through this study, we observed which keywords are most often used for each product type and found topics that express the characteristics of products and services using topic modeling. We derived keywords such as "professional" and "technician" which are topics that suggest the excellence of the service provider in the review of service products. Further, we identified adjectives with positive connotations such as "favorite", "fine", "fun", "nice", "smart", "unlimited", and "useful" from Amazon Eco review, an integrated product and service. Using the cluster analysis, the entire review was clustered into three groups, and three product type reviews exclusively resulted in belonging to each different cluster. The study analyzed the differences whereby consumer needs are expressed differently in reviews depending on the type of product and suggested that it is necessary to differentiate product planning and marketing promotion according to the product type in practice.

• Annual Conference on Human and Language Technology
• /
• 2019.10a
• /
• pp.429-433
• /
• 2019

스마트폰 보급의 확산으로 제품 구매 시 웹 사이트 및 SNS를 이용하여 제품 리뷰를 참고하는 소비자들이 증가하고 있다. 전자 상거래 사이트의 제품 리뷰는 구매 예정자들에게 유용한 정보로 활용되곤 한다. 하지만 구매 예정자가 직접 제품에 대한 리뷰 데이터를 찾아 전체 내용을 일일이 읽고 분석해야하기 때문에 시간이 오래 걸릴뿐만 아니라 가공되지 않는 데이터가 줄 수 있는 정보는 한정적이다. 또한 이러한 리뷰들은 상품의 특징을 파악하기에도 어려움이 있다. 본 논문에서는 제품의 주요 이슈를 추출하고 주요 이슈에 대한 감성 분석과 감성 요약을 통해 제품 분석 및 평가를 제공하는 시스템을 설계 및 구현하였다. 이를 휴대폰 제품에 적용하여 구축한 시스템을 통해 소비자가 방대한 양의 제품의 리뷰 데이터를 분석할 필요 없이 제품의 주요 이슈와 가공된 분석 결과를 시각적으로 빠르게 제공받을 수 있음을 보였다.

• The Journal of Society for e-Business Studies
• /
• v.16 no.3
• /
• pp.93-112
• /
• 2011

For the success of an online retail market, it is important to allow consumers to get more helpful reviews by figuring out the factors determining the helpfulness of online reviews. On the basis of elaboration likelihood model, this study analyzes which factors determine the helpfulness of reviews and how the factors affecting the helpfulness of an online consumer review differ for product price. For this study, 75,226 online consumer reviews were collected from Amazon.com. Furthermore, additional information on review messages was also gathered by carrying out a content analysis on the review messages. This study shows that both of peripheral cues such as review rating and reviewer's credibility and central cues such as word count of review message and the proportion of negative words influence the helpfulness of review. In addition, the result of this study reveals that each consumer focuses on different information sources of reviews depending on the product price.

• 한국IT서비스학회:학술대회논문집
• /
• 2008.11a
• /
• pp.431-434
• /
• 2008

본 연구에서 우리는 웹 사이트들에서 제품에 대한 사용자들의 리뷰 정보를 수집하고, 수집한 정보들을 분석 및 정련하여 사용자들에게 보이는 서비스에 대해 논하고자 한다. 특정 제품에 대한 리뷰 정보들은 로봇 시스템에 의해 수집되고, 특정 제품에 대한 전체적인 평가 스코어는 두 가지 다른 종류의 스코어들을 고려하여 계산된다. 첫 번째 스코어는 정량적인 스코어(quantitative score)로 각 리뷰들로부터 얻어지는 이른바 별점 값들의 가중 평균값(weighted average)으로 계산된다. 두 번째 스코어는 정성적인 스코어(qualitative score)로, 본 연구에서 제안된 서비스는 각 리뷰들의 텍스트 설명을 자연 언어 처리 기법으로 분석하여 정성적 스코어를 계산한다. 우리는 이러한 스코어 계산 모델에 따라 MP3 플레이어와 Personal Digital Assistant (PDA)에 대해 서비스 시스템 RELLENOS를 설계 및 구현하였다. RELLENOS는 69 개에 달하는 온라인 리뷰 사이트들에서 수집된 정보들을 토대로 정량적인 값과 정성적인 값을 계산하여 서비스를 성공적으로 수행하였다.

• The Journal of the Korea Contents Association
• /
• v.22 no.5
• /
• pp.1-15
• /
• 2022

In this study, text analysis was performed on the mealkit product review data to identify factors affecting the evaluation of the mealkit product. The data used for the analysis were collected by scraping 334,498 reviews of mealkit products in Naver shopping site. After preprocessing the text data, wordclouds and sentiment analyses based on word frequency and normalized TF-IDF were performed. Logistic regression model was applied to predict the polarity of reviews on mealkit products. From the logistic regression models derived for each product category, the main factors that caused positive and negative emotions were identified. As a result, it was verified that text analysis can be a useful tool that provides a basis for maximizing positive factors for a specific category, menu, and material and removing negative risk factors when developing a mealkit product.

• Journal of Internet of Things and Convergence
• /
• v.8 no.5
• /
• pp.47-54
• /
• 2022

The development of the Internet of Things led to new services that did not exist before. This required a change to the existing network. This study aims to verify the service quality, satisfaction, repurchase intention relationship, and the moderating effect of online reviews of Chinese consumers using fashion shopping malls. The results of the study showed that from the perspective of consumers in their 20s and 30s in China, the type, reliability, convenience, and interaction of service quality had a positive effect on customer satisfaction and repurchase intention. In addition, negative reviews among online reviews had a great influence on repurchase intention. Based on the results of the study, it will help improve the effect on online product reviews and in-depth understanding of the acceptance of online product reviews for online fashion shopping malls, and establish strategies for fashion companies to effectively manage online product reviews information.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.01a
• /
• pp.147-148
• /
• 2021

본 논문에서는 자바 스프링 Mybatis와 Firebase, Ajax, Script, DataBase를 활용해 테스트하길 원하는 IT 제품을 등록하는 기업과 이를 응모를 통해 추첨되어 테스트하고 리뷰를 남겨 기업에 도움을 주고 싶어 하는 일반 사용자를 모집하는 웹 사이트이다. 이 사이트는 자바 스프링 Mybatis를 기반으로 웹을 구성 하였고, 웹페이지에서 처리되는 데이터들은 데이터베이스로 저장된다. 기업이 제품을 등록하면 이를 사용자가 응모하고 관리자가 무작위로 추첨을 하면 해당 상품을 사용자가 받아보고 평가하여 리뷰를 남길 수 있고 기업은 해당 리뷰들을 좋은 점, 개선할 점을 나누어 확인하여 자신들의 제품을 개선할 수 있도록 한다.

• Informatization Policy
• /
• v.20 no.3
• /
• pp.3-17
• /
• 2013

As electronic marketplaces grow and a large number of consumers exchange their opinions on products and services on the Internet, many studies have been conducted in the area of online consumer reviews. This paper analyzes the research trend of the online consumer reviews by investigating those studies in an attempt to provide future research directions. Many researchers have focused on the effects of online reviews on consumer behaviors as well as the usefulness of the online reviews. In particular, review contents, characteristics of reviewers/consumers and features of products/services have been identified as influencing factors on the effects of the online consumer reviews. For the review contents, the number and the volume of the contents have increasing effects on the online reviews, while the direction (positive vs. negative) of the contents has resulted in conflicting effects of the review. The reputation and trustfulness of reviewers, consumers' prior knowledge on the products, consumers' product involvement, and types of the products were investigated as these factors influence the effectiveness of the online consumer reviews. Social media (such as Facebook and Twitter) nowadays play an important role to disseminate online reviews among consumers. Thus, it is necessary to study how social media influence the effects of online reviews on consumers. Since some firms abuse the online reviews for their own sakes, we recognize the necessity for empirical studies on the side effects of the online reviews.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.89-93
• /
• 2014

Reviews are very important for potential consumer' making choices. They are also used by manufacturers to find problems of their products and to collect competitors' business information. But someone write fake reviews to mislead readers to make wrong choices. Therefore detecting fake reviews is an important problem for the E-commerce sites. Support Vector Machines (SVMs) are very important text classification algorithms with excellent performance. In this paper, we propose a new incremental algorithm based on weight and the extension of Karush-Kuhn-Tucker(KKT) conditions and Convex Hull for online Review Spam Detection. Finally, we analyze its performance in theory.