• Title/Summary/Keyword: 상품평가

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Dictionary-Based Opinion Features Extraction and Classification of Korean Product Reviews (사전기반의 한국어 상품 리뷰 의견표현 자질 추출 및 분류시스템)

  • Sangguen Yuk
    • Proceedings of the Korea Information Processing Society Conference
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    • 2008.11a
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    • pp.631-634
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    • 2008
  • 인터넷을 이용한 사람들의 사회 참여가 확대되면서 다양한 의견(Opinion)들이 급속도로 증가하고 있으며 이러한 의견을 분석하여 유용한 정보로 활용하기 위한 연구가 활발히 진행되고 있다. 그 중에서도 상품리뷰는 기업에서 연구, 개발, 마케팅의 주요 자료로 사용되고 있으며 사용자가 상품의 구매를 결정하는 중요한 요인 중 하나로 작용하고 있다. 본 논문에서는 한국어로 이루어진 상품 리뷰를 분석하여 의견 자질(Feature)을 추출하고 분류(Classification)하는 시스템을 설계하고 구현하였다. 한글 의견 자질 추출을 위하여 먼저 한글 상품 리뷰를 분석하여 의견 사전을 구축하였다. 의견 사전으로는 의견 자질과 의견 어휘, 독립의견어휘, 의견 숙어, 부정어 등의 각기 다른 세부 사전을 구축하여 리뷰 분석 시 단계적으로 적용하여 정확도를 높일 수 있도록 설계하였다. 이렇게 구현된 시스템을 평가하기 위하여 각기 다른 3개의 도메인에서 실제 한국어 리뷰를 수집하여 실험을 수행하였으며 자질 추출에서는 평균 78.86% 정확률, 61.41% 재현율을, 극성 분류에서는 평균 69.46% 정확률, 42.26% 재현율을 나타냈다.

An Assessment of Technological Competitiveness in Core Products of Foreign Design & Construction markets (해외 유망 건설상품의 기술 경쟁력 평가)

  • Choi, Seok-In;Kim, Sang-Bum;Lee, Young-Whan;Kim, Woo-Young;Jang, Hyoun-Seung
    • Korean Journal of Construction Engineering and Management
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    • v.9 no.1
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    • pp.107-117
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    • 2008
  • In this study, surveys and interviews are used to evaluate technological competitiveness of each product with respect to that of foreign leading firms, for seven leading domestic construction products which have been determined to have competitive edge in offshore markets, Such evaluation provides a more in depth study than previously conducted research, and is meaningful in that corporate level, rather than industry level, perspective is projected. Major findings of such evaluations are the following. First, as expected, it has been evaluated that domestic technological competitiveness in desalination plant and power plant has reached the point where it can compete with foreign leading firms. Moreover, a noteworthy result of the evaluation is that development program sector, including urban development of satellite cities, has reached considerable level of competitiveness in offshore market. In the case of the development market, domestic firms have accumulated sufficient experience in domestic market and engineering technology is not a decisive factor as in plant sector, and these factors lead to such an evaluation. Second, in the cases of gas, oil refinery and petro-chemical plants, domestic products' technological competitiveness that can contest in offshore market is still centered around production and construction. On the other hand, there are still weaknesses in license technology and basic design capabilities, which constitute the "value added" area. Third, skyscrapers, a promising product in offshore construction market and a product group which domestic firms have much performance record and projects in progress both in domestic and offshore markets, are considered. While direct comparison between skyscrapers and plant sector is not feasible, with the exception of production and construction, overall domestic capability in this sector has been assessed to be the lowest amongst those products that were surveyed. Fourth, it has been indicated that competitiveness is relatively higher in common technology than in key technology. In project management capability, it has been assessed that there are weaknesses in procedure document area. Also, a characteristic is the point that low overall assessments have been given across all product groups for corporate and management areas, not technological areas. Especially, financing, contracting/claim, risk management and investment on research and development received low evaluations. Fifth, it has been assessed that overall corporate and governmental supports are weak. This result is especially evident for corporate management and support areas across all product groups surveyed.

Retrieving Minority Product Reviews Using Positive/Negative Skewness (긍정/부정 비대칭도를 이용한 소수상품평의 검색)

  • Cho, Heeryon;Lee, Jong-Seok
    • KIPS Transactions on Software and Data Engineering
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    • v.4 no.3
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    • pp.121-128
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    • 2015
  • A given product's online product reviews build up to form largely positive or negative reviews or mixed reviews that include both the positive and negative reviews. While the homogeneously positive or negative reviews help readers identify the generally praised or criticized product, the mixed reviews with minority opinions potentially contain valuable information about the product. We present a method of retrieving minority opinions from the online product reviews using the skewness of positive/negative reviews. The proposed method first classifies the positive/negative product reviews using a sentiment dictionary and then calculates the skewness of the classified results to identify minority reviews. Minority review retrieval experiments were conducted on smartphone and movie reviews, and the F1-measures were 24.6% (smartphone) and 15.9% (movie) and the accuracies were 56.8% and 46.8% when the individual reviews' sentiment classification accuracies were 85.3% and 78.8%. The theoretical performance of minority review retrieval is also discussed.

A Sentiment Analysis Algorithm for Automatic Product Reviews Classification in On-Line Shopping Mall (온라인 쇼핑몰의 상품평 자동분류를 위한 감성분석 알고리즘)

  • Chang, Jae-Young
    • The Journal of Society for e-Business Studies
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    • v.14 no.4
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    • pp.19-33
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    • 2009
  • With the continuously increasing volume of e-commerce transactions, it is now popular to buy some products and to evaluate them on the World Wide Web. The product reviews are very useful to customers because they can make better decisions based on the indirect experiences obtainable through the reviews. Product Reviews are results expressing customer's sentiments and thus are divided into positive reviews and negative ones. However, as the number of reviews in on-line shopping increases, it is inefficient or sometimes impossible for users to read all the relevant review documents. In this paper, we present a sentiment analysis algorithm for automatically classifying subjective opinions of customer's reviews using opinion mining technology. The proposed algorithm is to focus on product reviews of on-line shopping, and provides summarized results from large product review data by determining whether they are positive or negative. Additionally, this paper introduces an automatic review analysis system implemented based on the proposed algorithm, and also present the experiment results for verifying the efficiency of the algorithm.

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The Blog Polarity Classification Technique using Opinion Mining (오피니언 마이닝을 활용한 블로그의 극성 분류 기법)

  • Lee, Jong-Hyuk;Lee, Won-Sang;Park, Jea-Won;Choi, Jae-Hyun
    • Journal of Digital Contents Society
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    • v.15 no.4
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    • pp.559-568
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    • 2014
  • Previous polarity classification using sentiment analysis utilizes a sentence rule by product reviews based rating points. It is difficult to be applied to blogs which have not rating of product reviews and is possible to fabricate product reviews by comment part-timers and managers who use web site so it is not easy to understand a product and store reviews which are reliability. Considering to these problems, if we analyze blogs which have personal and frank opinions and classify polarity, it is possible to understand rightly opinions for the product, store. This paper suggests that we extract high frequency vocabularies in blogs by several domains and choose topic words. Then we apply a technique of sentiment analysis and classify polarity about contents of blogs. To evaluate performances of sentiment analysis, we utilize the measurement index that use Precision, Recall, F-Score in an information retrieval field. In a result of evaluation, using suggested sentiment analysis is the better performances to classify polarity than previous techniques of using the sentence rule based product reviews.

Deep learning-based product image classification system and its usability evaluation for the O2O shopping mall platform (딥 러닝 기반 쇼핑몰 플랫폼용 상품 이미지 자동 분류 시스템 및 사용성 평가)

  • Sung, Jae-Kyung;Park, Sang-Min;Sin, Sang-Yun;Kim, Yung-Bok;Kim, Yong-Guk
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.17 no.3
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    • pp.227-234
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    • 2017
  • In this paper, we propose a system whereby one can automatically classifies categories based on image data of the products for a shopping mall platform. Many products sold within internet shopping malls are classified their category defined by the same use of product names and products. However, it is difficult to search by category classification when the classification of the product is uncertain and the product classified by the shopping mall seller judgment is different from the purchasing user judgment. We proposes classification and retrieval method by Deep Learning technique solely using product image. The system can categorize products by using their images and its speed and accuracy are quantified using test data. The performance is evaluated with the test data. In addition, its usability is tested with the participants.

User Sentiment Analysis on Amazon Fashion Product Review Using Word Embedding (워드 임베딩을 이용한 아마존 패션 상품 리뷰의 사용자 감성 분석)

  • Lee, Dong-yub;Jo, Jae-Choon;Lim, Heui-Seok
    • Journal of the Korea Convergence Society
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    • v.8 no.4
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    • pp.1-8
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    • 2017
  • In the modern society, the size of the fashion market is continuously increasing both overseas and domestic. When purchasing a product through e-commerce, the evaluation data for the product created by other consumers has an effect on the consumer's decision to purchase the product. By analysing the consumer's evaluation data on the product the company can reflect consumer's opinion which can leads to positive affect of performance to company. In this paper, we propose a method to construct a model to analyze user's sentiment using word embedding space formed by learning review data of amazon fashion products. Experiments were conducted by learning three SVM classifiers according to the number of positive and negative review data using the formed word embedding space which is formed by learning 5.7 million Amazon review data.. Experimental results showed the highest accuracy of 88.0% when learning SVM classifier using 50,000 positive review data and 50,000 negative review data.

A Study on Evaluation Indices for Testing PoP of Mobile Phones (이동 통신 단말기의 상품력 검증을 위한 평가 지표에 대한 연구)

  • Ko, Seoung-Gon
    • The Korean Journal of Applied Statistics
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    • v.23 no.6
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    • pp.1035-1045
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    • 2010
  • Products and/or services should be objectively verified in terms of the technological and use-conditional considerations before entering a market. Every organization or company tries to find the better procedure and method for checking the core needs of customers based on their experience in the market and looks for continuous ways to evaluate the power of products and services(PoP). They also prefer the overall evaluation of indices that could reflect various customer needs, rather than a separate evaluation index for each characteristic of the product or service. S. Ko (2008) proposes a Multi-characteristics Sigma Level(MSL) that can simultaneously evaluate many characteristics of a product or service. In this research, using MSL and a new Blue Ocean Index(BOI), an application of NPS, mobile phone field test is considered from a practical and statistical point of view.

A New Similarity Measure for e-Catalog Retrieval Based on Semantic Relationship (의미적 연결 관계에 기반한 전자 카탈로그 검색용 유사도 척도)

  • Seo, Kwang-Hun;Lee, Sang-Goo
    • Journal of KIISE:Databases
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    • v.34 no.6
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    • pp.554-563
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    • 2007
  • The e-Marketplace is growing rapidly and providing a more complex relationship between providers and consumers. In recent years, e-Marketplace integration or cooperation issues have become an important issue in e-Business. The e-Catalog is a key factor in e-Business, which means an e-Catalog System needs to contain more large data and requires a more efficient retrieval system. This paper focuses on designing an efficient retrieval system for very large e-Catalogs of large e-Marketplaces. For this reason, a new similarity measure for e-Catalog retrieval based on semantic relationships was proposed. Our achievement is this: first, a new e-Catalog data model based on semantic relationships was designed. Second, the model was extended by considering lexical features (Especially, focus on Korean). Third, the factors affecting similarity with the model was defined. Fourth, from the factors, we finally defined a new similarity measure, realized the system and verified it through experimentation.

Effect of Unplanned Haptic Experience on Product Evaluation (계획되지 않은 햅틱 경험이 상품의 가치 평가에 미치는 영향)

  • Park, Yong Bae;Park, JuHwa;Cho, KwangSu
    • Design Convergence Study
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    • v.14 no.5
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    • pp.47-56
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    • 2015
  • People often use haptic experience as a basis for their preference decisions and value judgments, assuming that haptic experience with a product results from the properties of the products. However, research has suggested that unplanned haptic experience, which does not arise from the properties of the product itself, can also influence people's preference and value evaluation (Ackerman, Nocera, & Bargh, 2010). In this study, in order to verify (1) if such unplanned or accidental haptic experience changes user's cognitive tendency and (2) if accidental haptic experience leads to misattribution of the cause of haptic experience, two hypotheses were suggested and empirically investigated. Participants of the experiment were exposed to certain products on a display of a tablet PC and asked to decide on the maximum price they were willing to pay for each product. The products displayed on the screen were made up of either soft material or hard material. Results of the experiment revealed that accidental haptic experience had an effect on participants' value evaluation of products via altering their cognitive inclinations. Possible applicability of accidental haptic experiences that occur in various situations were discussed.