• 식품기술
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• 제16권2호
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• pp.78-179
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• 2003

Nature 본지는 1896년에 창간된 이래 130년 이상 과학출판에 있어 지대한 영향력을 끼쳐온 과학잡지로 최근 Nature 본지 이외에도 자매지인 Nature Biotechnology(1983년 창간), Nature Cell Biology(1999), Nature Genetics(1992), Nature Meterials(2002), Nature Immunology(1999), Nature Medicine(1995), Nature Neuroscience(1998), Nature Structural Biology(1994)의 8개의 Nature Reserch Journal이 있으며 6개의 Nature Reviews(Nature Reviews Cancer, Nature Reviews Drug Discovery, Nature Reviews Genetics, Nature Reviews Immunology, Nature Reviews Molecular Cell Biology, Nature Reviews Neuroscience)가 있다. Nature 본지는 Editorials, News, News Feature, Correspondence, Commentary, Books and Arts, Concepts, News and Views, Brief Communications, Review Article, Articles, Letter to Nature, Naturejob 등으로 구성된 주간지로 과학전반에 걸친 중요한 성과를 즉각적으로 출판할 기회 뿐 아니라 과학에 관한 뉴스나 이슈 등에 대한 토론의 장을 제공하기도 한다. Nature 본지의 2002년 ISI impact factor는 30.432로 SCI에 등재된 저널중에서 매우 높은 수위를 차지하고 있다. 따라서 Nature 본지에 게재된 식품 및 유관 분야에 대한 조사 분석 결과를 게재하오니 향후 투고 방향 설정에 참고하시기 바랍니다. 식품기술에서는 계속적으로 update할 예정입니다.

• Journal of Smart Tourism
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• 제3권3호
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• pp.29-40
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• 2023

This research aims to understand the role of online reviews in the local search context by examining the effects of reviews on the representation of tourism businesses on local search platforms (LSPs). By simulating tourists' local searches for restaurants on three LSPs, namely Google, Bing, and Yelp, this study examines how different ranking results are generated across the platforms and how online reviews contribute to the differences. The findings suggest that online reviews are incorporated into LSPs as ranking factors and, thus, affect tourists' decision-making by influencing the information search results in the local search context. As one of the earliest studies on local search, this study discusses how the existing knowledge about the role of online reviews in tourists' decision-making needs to be reevaluated in mobile and more dynamic environments, and offers practical implications for tourism businesses' search engine marketing.

• Asia pacific journal of information systems
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• 제27권4호
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• pp.216-232
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• 2017

This study's objective is to use the construal-level theory (CLT) to explore the effect of the utility of online travel reviews on tourists' perception. To accomplish this goal, online travel reviews are divided into two different categories based on concreteness, and the usefulness of each review is compared with the temporal dimension of psychological distance. The results show that close future tourists are more influenced by concrete reviews than abstract reviews; however, the far future tourists are more influenced by abstract reviews than concrete reviews. Based on these results, theoretical and practical implications are discussed, and suggestions are made for future research.

• Journal of Information Technology Applications and Management
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• 제31권1호
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• pp.57-77
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• 2024

The aim of this research is to examine the impact of trust in reviews. Expertise, enjoyment, recency, and usefulness-four aspects of reviews-are designated as independent variables, and trust in reviews has been chosen as the mediating variable. The dependent variables are trust in firms and trust in products. For explaining the flow of trust, this study uses the theory of Trust Transfer. The study's findings demonstrated that customer trust in a product leads to consumer trust in a company, which is derived from trust in reviews. Reviews were found to be important from a practical standpoint. Furthermore, it was discovered that a product's category or features would have an impact on how reviews are trusted.

• 지능정보연구
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• 제22권3호
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• pp.129-142
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• 2016

전자상거래에서 소비자들의 구매 의사결정에 판매 제품을 이미 구매하여 사용한 고객의 리뷰가 중요한 영향을 미치고 있다. 전자상거래 업체들은 고객들이 제품 리뷰를 남기도록 유도하고 있으며, 구매고객들도 적극적으로 자신의 경험을 공유하고 있다. 한 제품에 대한 고객 리뷰가 너무 많아져서 구매하려는 제품의 모든 리뷰를 읽고 제품의 장단점을 파악하는 것은 무척 힘든 일이 되었다. 전자상거래 업체들과 연구자들은 텍스트 마이닝을 활용하여 리뷰들 중에서 유용한 리뷰들의 속성을 파악하거나 유용한 리뷰와 유용하지 않은 리뷰를 미리 분류하는 노력을 수행하고 있다. 고객들에게 유용한 리뷰를 필터링하여 전달하는 방안이다. 본 연구에서는 문서-단어 매트릭스에서 단어의 제거 기준으로 온라인 고객 리뷰가 유용한 지, 그렇지 않은지를 구분하는 문제에서 단어들이 유용 리뷰 집합과 유용하지 않은 리뷰집합에 중복하여 등장하는 정도를 측정한 중립도를 제시한다. 제시한 중립도를 희소성과 함께 분석에 활용하여 제거할 단어를 선정한 후에 각 분류 알고리즘의 성과를 비교하였다. 최적의 성과를 보이는 중립도를 찾았으며, 희소성과 중립도에 따라 단어를 선택적으로 제거하였다. 실험은 Amazon.com의 'Cellphones & Accessories', 'Movies & TV program', 'Automotive', 'CDs & Vinyl', 'Clothing, Shoes & Jewelry' 제품 분야 고객 리뷰와 사용자들의 리뷰에 대한 평가를 활용하였다. 전체 득표의 수가 4개 이상인 리뷰 중에서 제품 카테고리 별로 유용하다고 판단되는 1,500개의 리뷰와 유용하지 않다고 판단되는 1,500개의 리뷰를 무작위로 추출하여 연구에 사용하였다. 데이터 집합에 따라 정확도 개선 정도가 상이하며, F-measure 기준으로는 두 알고리즘에서 모두 희소성과 중립도에 기반하여 단어를 제거하는 방안이 더 성과가 높았다. 하지만 Information Gain 알고리즘에서는 Recall 기준으로는 5개 제품 카테고리 데이터에서 언제나 희소성만을 기준으로 단어를 제거하는 방안의 성과가 높았으며, SVM에서는 전체 단어를 활용하는 방안이 Precision 기준으로 성과가 더 높았다. 따라서, 활용하는 알고리즘과 분석 목적에 따라서 단어 제거 방안을 고려하는 것이 필요하다.

• 대한한방소아과학회지
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• 제34권1호
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• pp.26-36
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• 2020

Objectives The purpose of this study is to assess the reporting quality and methodological quality of systematic reviews from the Journal of Pediatrics of Korean Medicine. Methods Systematic reviews were selected from the Journal of Pediatrics of Korean Medicine (JPKM) by utilizing Oriental Medicine Advanced Searching Integrated System (OASIS) and JPKM homepage. Two independent researchers assessed the reporting quality through Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline checklist, and assessed the methodological quality of systematic review through Assessment of Multiple Systematic Reviews (AMSTAR) 2 tool checklist. Results Four systematic reviews were finally selected for the assessment. When assessed by PRISMA, three literatures were little insufficient, and one literature was sufficient. When assessed by AMSTAR 2, three literatures were moderate quality, and one literature was critically low quality. Also, all of the reviews had no information about 'Protocol and registration', 'publication bias', and 'conflicts of interest'. Conclusions Systematic review is important for Journal of Pediatrics of Korean Medicine and Korean Medicine Society. Efforts are needed to improve the reporting and methodological quality of the systematic reviews through PRISMA and AMSTAR 2.

• 한국IT서비스학회지
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• 제16권3호
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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.

• 한국정보시스템학회지:정보시스템연구
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• 제31권4호
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• pp.161-187
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• 2022

Purpose This study analyzed the topic of game review contents and how the characteristics of game reviews affect the reviews helpfulness. In addition, this study explore the content of game reviews according to the game's sales strategy such as early access strategy and releasing without early access. Design/methodology/approach We collected a list of 3,572 action genre games released in 2020. 58,336 online reviews were collected by random sampling 50 reviews in each games, and topic modeling was performed on those reviews. We dynamized the results of topic modeling and analyzed the effect on review helpfulness with multiple regression analysis. Findings The results of analysis indicate that the longer the review is or the shorter the time it is written, the more helpful the review is. In addition the topic with positive and negative review has a significant effect on the review helpfulness. As a result of exploratory analysis, games from early access had relatively fewer reviews of story-related topics than games that were released without early access. These findings can present direct guidelines for collecting specific opinions from customers in the game industry when releasing games.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.838-855
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• 2019

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 춘계학술대회
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• pp.79-81
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• 2014

전자 상거래 리뷰 정보에 대한 소비자들의 의존도가 증가하고 있다. 제품 리뷰는 잠재적인 고객의 구매 결정에 있어 중요한 결정 요소이다. 제품 리뷰는 또한 상품 제조사들이 자신들의 제품에 대한 문제점을 발견하고 자신들의 경쟁자들에 대한 경쟁 정보를 수집할 수 있도록 해준다. 불행히도 많은 온라인 제품 정보들이 그 제품에 대한 진짜 고객들에 의해 만들어지지 않은 것이라는 것은 잘 알려진 사실이다. 리뷰를 쓰는 사람들은, 특정 제품의 평판을 떨어뜨리기 위해 가짜로 부정적인 리뷰를 쓰거나, 특정 제품에 대해 부당하게 긍정적인 리뷰를 써서 그 제품을 홍보하기도 한다. 이러한 리뷰들을 가짜 리뷰라고 한다. 가짜 리뷰 판별 기법은 가짜 리뷰를 판별하고 삭제하여 진실한 리뷰들만 독자에게 제공하기 위한 기법이다. 현재까지 이 문제에 대한 연구는 많이 발표되지 않았다. 본 논문에서, 우리는 관련 연구들을 조사하고 가짜 리뷰 판별 기법들에 대해 간단히 조망해 보고자 한다. 웹 스팸 및 이메일 스팸과 같은 가짜 리뷰 판별과 관련된 연구들을 소개한다. 그리고, 가짜 리뷰들을 판별하기 위한 방법들을 소개하고 요약한다. 마지막으로 가짜 리뷰 판별에 대한 연구 추세들로 결론을 맺는다.