• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.57-78
• /
• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• The Journal of Society for e-Business Studies
• /
• v.21 no.2
• /
• pp.151-161
• /
• 2016

In the era of the Web 2.0, characterized by the openness, sharing and participation, it is easy for internet users to produce and share the data. The amount of the unstructured data which occupies most of the digital world's data has increased exponentially. One of the kinds of the unstructured data called personal online product reviews is necessary for both the company that produces those products and the potential customers who are interested in those products. In order to extract useful information from lots of scattered review data, the process of collecting data, storing, preprocessing, analyzing, and drawing a conclusion is needed. Therefore we introduce the text-mining methodology for applying the natural language process technology to the text format data like product review in order to carry out extracting structured data by using R programming. Also, we introduce the data-mining to derive the purpose-specific customized information from the structured review information drawn by the text-mining.

• Management & Information Systems Review
• /
• v.34 no.2
• /
• pp.79-95
• /
• 2015

The purpose of this paper is to evaluate the effects of perceived risk and brand attitude on licensing brands comparing with non-licensed brands(virtual brand). Data was collected through a self-administered questionnaire in quasi-experimental design setting. I designed the experimental setting that there were two virtual companies to sell the luxury bags(symbolic goods) or cruise tour(experiential goods) and to launch their goods with own brand or licensed brand. The experimental groups were composed of women consumers who were familiar with consuming experiential goods and symbolic goods. Results from the experiment suggest that consumer's perceived risk on brands gives a negative impact on brand attitude. And congruence in goods types and licensed brand values leads to difference in the level of perceived risk. In experiential goods, brand licensing from famous and experiential brands can reduce perceived risk. But in symbolic goods, brand licensing effect which reduces the perceived risk is less effective than in experiential goods. This findings suggest that brand licensing may lower the level of consumer's perceived risk, but incongruity in goods type and brand value may result in strategic failure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.771-772
• /
• 2015

소셜미디어 상 고객들이 쏟아내는 말을 실시간으로 분석, 조사하는 방법으로 버즈 모니터링 이라는 시스템을 이용하여 웹상의 다양한 정보를 자동으로 검색하고 수집하고 있다. 본 논문에서는 여행사에 관해 소셜 미디어 상의 빅 데이터를 이용하여 보다 정확하고 효율적인 정보 수집과 분석이 가능하도록 하기위한 분석 모델을 제안하고 실제 국내 여행사에 관해 비교 분석한다. 먼저 여행사별 인지도,이미지와 선호도 분석을 하고 관광관련 상품과 서비스에 대한 분석과 함께 소비자 분석으로서 관광의 목적, 동행인 등 소비자의 생활패턴에 대한 분석을 한다. 또한 여행사 관련 영향력자 경향을 트위터 상에서 살펴본 결과 해당 여행사 이용경험자와 관련 뉴스를 제공하는 언론, 이벤트에 관심 있는 사용자들로 유형화 할 수 있었다.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.1 s.39
• /
• pp.293-300
• /
• 2006

The Recommender systems help users to find and evaluate items of interest. Such systems have become powerful tools in the domains from electronic commerce to digital libraries and knowledge management. Sellers can recommend products to customers with the prediction of future buying behavior on the basis of the consumer's population statistics and past selling behavior. In this paper, we are describing the design and the development of personalization recommender system which increases satisfaction level of customers by searching products to reflect the pattern and propensity of customers properly. The suggested system supplies the real-time analysis service to predict the customers purchase situation by applying the association rule of the data mining.

• Proceedings of The KACE
• /
• 2017.08a
• /
• pp.227-230
• /
• 2017

전 세계적으로 과학, 기술, 공학, 예술, 수학이 융합된 STEAM교육이 강조되고 있는 가운데 우리나라도 STEAM교육의 효율성을 높여줄 수 있는 콘텐츠에 대한 관심과 적용이 늘어가고 있다. 본 논문은 "공업입지론" 내용을 바탕으로 개발된 에듀테인먼트 콘텐츠의 교육적 효과를 향상시키고자 분석 피드백 기능을 강화하였다. 강화된 분석 피드백 기능은 첫째, 학습자가 공장 선택 조건과 각 공장의 장점 확인, 둘째, 상품의 운송조건 제시, 셋째, 학습 결과에 대한 분석이다. 이에 학습자가 콘텐츠를 통해 학습하는 과정에서 분석 피드백을 제공 받아 학습에 대한 이해도 등 교육적 효과를 향상 시키고자 한다.

• Culinary science and hospitality research
• /
• v.15 no.1
• /
• pp.105-119
• /
• 2009

The elevated standard of living and increased interest in health and well-being have caused customers to expect a role. Consumers are more and more interested in food service merchandise development. The purpose of this paper is to perform a discriminant analysis of merchandise development policies based on customer participation strategies. Statistical techniques employed included the reliability analysis and the discriminant analysis. The current thesis is based on the 350-questionnaire-survey conducted from July 1 to July 31, 2008 at five food service companies in Seoul and Gwangju. The data collected for this study was analyzed with frequency analysis, reliability analysis, validation analysis, factor analysis, and discriminant analysis using the SPSS 12.0 package program. The results of the test of the hypotheses can be summarized as follows: the analysis shows that there is significant difference between the active groups and the passive groups in all the merchandise development related factors, market directivity, and customer satisfaction directivity. Thus, food service management needs to apply customer participation strategies aggressively.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.9
• /
• pp.39-47
• /
• 2012

The opinion of user on a certain product is expressed in positive/negative sentiments for specific features of it. In some cases, they are expressed for a holistic part of homogeneous specific features, or expressed for product itself. Therefore, in the area of opinion mining, name of opinion features to be extracted are specific feature names, holonyms for theses specific features, and product names. However, when the opinion target is described with product name or holonym, sometimes it may not match feature name of opinion sentence to true opinion target intended by the reviewer. In this paper, we present a method to extract opinion targets from opinion sentences. Most importantly, we propose a method to extract true target from the feature names mismatched to a intended target. First, we extract candidate opinion pairs using dependency relation between words, and then select feature names frequently mismatched to opinion target. Each selected opinion feature name is replaced to a specific feature intended by the reviewer. Finally, in order to extract relevant opinion features from the whole candidate opinion pairs including modified opinion feature names, candidate opinion pairs are rearranged by the order of user's interest.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.10b
• /
• pp.49-51
• /
• 2001

데이터 마이닝은 KDD의 분야로서, 의미 있는 정보와 관심 있는 행동 패턴을 추출해 나가는 과정이다. WWW의 발전으로, 웹 데이터가 거대해지고 있다. 이러한 데이터 마이닝 분야에서도, 웹 사용 마이닝의 목적은 의미 있는 사용자 행동 패턴을 찾아내는 것이다. 특히 현재 전자상거래가 널리 활성화되고 있는 환경에서, 사용자의 특성을 발견해내는 것은 매우 중요한 부분이다. 사용자의 특성에 따라 사용자에게 상품을 추천하거나 메일을 보내는 것이나 사용자에게 적절하게 사이트를 구축하는 것이 가능하다. 전처리 과정을 통해서 추출된 트랜잭션 데이터를 모호한 사용자의 요구를 분석할 수 있는 퍼지 집합으로 변형시켜 Fuzzy Association Rule을 통해 분석한다. 그리고 분석된 결과에 대한 규칙을 사용자의 피드백을 통해서 다시 분석하는 과정을 거치게 된다. 사용자의 요구 사항을 적절히 반영할 수 있다.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.660-663
• /
• 2009

Consumers express how they evaluate what they purchased by writing reviews especially when they purchased products online. By analyzing the reviews about a product, it will be possible to find out what the consumers liked and disliked about the product. It will be also possible to identify the general consensus on what matters in purchaing certain product type such as a laptop if many reviews about many instances of a particular product type is analyzed. However, it takes a lot of time to manually analyzing the reviews. Thus, we propose to use two natural language processing oriented computational techniques to analyze a large number of reviews. The techniques are text classification and information extraction. We developed an review analysis system and conducted experiments against the reviews about the laptop computers posted on the Naver information portal.