• 품질경영학회지
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• 제47권1호
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• pp.97-108
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• 2019

Purpose: The purpose of this study is to propose a new Risk Priority Number(RPN) evaluation method which analyzes value of product functions by mining customer opinions in Social Network Service(SNS). Methods: A traditional RPN is measured by three evaluation standards (Severity, Occurrence, Detection) which are analyzed by manufacturing engineers and researchers. On the other hand, these standards are analyzed by customers' viewpoints through SNS opinion mining in this research. In order to extract customer feedbacks from textual data sets, the methodology in this paper implies natural language processing, hereby collecting product related data sets and analyzing the opinions automatically. An emotional polarity of an opinion indicates severity, while the number of negative opinion shows occurrence, and the entire number of customer opinion refers to detection. Results: The results of this study are as follows; As a result of the CRPN evaluation, it is confirmed that the features evaluated as risky are highly likely to be improved in the next series. Therefore, CRPN is an effective risk assessment model that reflects customer feedback. Conclusion: Reflecting customer feedback is a useful tool for risk assessment of the product as well as for developing new products and improving existing products.

• 한국병원경영학회지
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• 제18권3호
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• pp.1-26
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• 2013

To improve the competitiveness of the hospital provides high quality medical services in a hospital coordinator role is emphasized. This study on customer orientation of the role ambiguity in order to identify the impact of degree of customer orientation were analyzed for demographic differences. Dependent variable, customer orientation affects role ambiguity as independent variables, and regression analysis were set. And the control variables are set to support situational factors, customer orientation on the role ambiguity and hierarchical regression analysis was performed. Obtained through empirical results are as follows: First, according to the demographic characteristics of the hospital coordinator customer orientation, the difference between gender and medical subjects are not shown. Age, education, work experience, job title, and the hospital on the pattern of customer orientation has shown a difference. Second, according to the hospital coordinator role ambiguity about its impact on customer orientation analysis can be a role implementation, job implementation, opinion communication in achieving customer orientation was negatively affected. Third, role ambiguity, and customer orientation factors for the moderating effects of organizational support for the role of customer orientation can role implementation, job implementation, opinion communication was a statistically significant. Fourth, the role ambiguity factors and customer orientation for the administrative support for the moderating effect of customer orientation and role implementation is significant, but job implementation, opinion communication were statistically significant. Fifth, the role ambiguity factors and customer support for customer orientation and customer orientation for the moderating effects of role performance and the opinion communication was not statistically significant. However, job implementation was statistically significant. The limitations of this study are as follows: First, role ambiguity, situational factors and support due to limitations of the variable factors that may affect the customer orientation of a number of factors were excluded. So many exogenous variables in the measurement process can affect. Second, the variables measured as problems of self-assessment by the variable measuring the respondent's bias may occur. Third, This study is difficult to generalize. In other words, several areas of the province conducted by the empirical results of the survey as a limit on the overall generalization can follow.

• ETRI Journal
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• 제33권5호
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• pp.720-730
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• 2011

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.

• 한국정보통신학회논문지
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• 제15권1호
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• pp.133-140
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• 2011

특정 제품이나 서비스에 대한 네티즌의 의견들은 고객들의 구매 행위에서의 참고대상일 뿐만 아니라 기업 입장에서도 마케팅이나 경영전략을 수립하기 위한 중요한 자료가 될 수 있기 때문에 온라인 고객리뷰를 분석하는 것은 매우 중요하다. 본 논문에서는 비정형(unformatted) 데이터형인 자연어(natural language) 형태로 웹상에 게시된 고객 의견들을 분석할 수 있는 새로운 오피년마이닝 기법을 제안한다. 기존 데이터마이닝 기법 중의 하나인 연관규칙탐사 기법을 수정하여 오피년마이닝 과정에 보다 효율적이고 효과적으로 적용하기 위한 방안을 고찰하고 이를 기반으로 실제 시스템을 설계하고 구현하였다.

• 산업공학
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• 제12권4호
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• pp.496-505
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• 1999

Quality of Service(QoS) is the collective effect of service performances and has a direct impact on customer satisfaction. Although QoS is subjective, network performance parameters contributing to QoS can be measured physically. Therefore overall customer satisfaction for each test condition of the performance parameters is evaluated by asking respondents to indicate his or her opinion on a five-category rating scale i.e., excellent, good, fair, poor, and unsatisfactory. The opinion data resulting from the test can then be used to measure and analyze QoS from the customers' viewpoints. In this papaer, we consider two methods for analyzing the opinion data: MOS method and Cumulative Probability Curve method. The former evaluates an arithmetic mean of the opinion scores which quantify the surveyed opinions of respondents. The latter uses graphical and analytical models which are based on the distribution of the opinions rather than an arithmetic mean. The advantages, disadvantages, and an alternative of each method are discussed, together with future directions of research.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.652-657
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• 2018

With the rapid development of e-commerce, many customers can now express their opinion on various kinds of product at discussion groups, merchant sites, social networks, etc. Discerning a consensus opinion about a product sold online is difficult due to more and more reviews become available on the internet. Opinion Mining, also known as Sentiment analysis, is the task of automatically detecting and understanding the sentimental expressions about a product from customer textual reviews. Recently, researchers have proposed various approaches for evaluation in sentiment mining by applying several techniques for document, sentence and aspect level. Aspect-based sentiment analysis is getting widely interesting of researchers; however, more complex algorithms are needed to address this issue precisely with larger corpora. This paper introduces an approach of knowledge representation for the task of analyzing product aspect rating. We focus on how to form the nature of sentiment representation from textual opinion by utilizing the representation learning methods which include word embedding and compositional vector models. Our experiment is performed on a dataset of reviews from electronic domain and the obtained result show that the proposed system achieved outstanding methods in previous studies.

• 경영정보학연구
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• 제17권1호
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• pp.49-64
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• 2015

SNS의 확산으로 온라인 상점에서는 상품에 대한 주관적인 의견이 내포되어 있는 고객리뷰 정보가 빠르게 생성되고 확산되어 다른 고객들에게 큰 영향을 미치고 있다. 이와 더불어, 고객들의 긍정적 또는 부정적 의견을 분석하여 개선방안을 모색하려는 오피니언마이닝(opinion mining)이 주목 받고 있다. 고객리뷰에 내포된 감성정보를 가진 용어들은 감성분류를 하는데 가장 중요한 역할을 하기 때문에 영향력이 높은 용어를 선별하는 것이 가장 중요하다. 본 연구에서는 품사태깅을 이용하여 최적의 용어들을 선별하고 용어정보에 기반한 문서수준에서의 감성분류모형을 제안하고자 한다. 고객리뷰의 감성분류모형에 대표적인 기계학습기법인 SVM을 적용하고, SVM의 입력변수 선정과정에 품사태깅 방식과 용어추출기법을 다르게 조합하고 사용하여 긍정적/부정적 문서를 분류하였다. 본 연구에서 제안한 감성분류모형의 성과를 검증하기 위해 아마존(Amazon.com)의 영화와 도서에 대한 고객리뷰 80,000개를 수집하여 불필요한 용어들을 제거한 후 품사태깅을 통해 용어를 추출하였다. 추출된 용어는 문서빈도, TF-IDF, 정보획득량, 카이제곱 통계량의 값을 산출하여 값을 통해 용어들을 순위화하고, 각 상위 20개에 해당하는 최적의 용어를 선정한 후 SVM을 이용하였다. 제안된 감성분류모형을 통해 기존 연구에서 언급한 형용사만을 사용한 예측변수와 4품사를 사용한 예측변수에서의 실험결과를 통해 비교 분석하였다. 카이제곱 통계량 기반의 감성분류모형이 다른 모형보다 예측성과가 가장 우수하게 나타나는 것을 확인할 수 있었다. 본 연구에서 제안된 문서수준에서의 용어기반 감성분류모형을 이용함으로써 온라인 상점에서의 서비스 개선과 경쟁력 확보에 많은 도움이 될 것으로 기대된다.

• 한국콘텐츠학회논문지
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• 제12권4호
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• pp.76-85
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• 2012

오피니언마이닝 기법은 대량의 고개리뷰들에 나타나는 핵심개체 또는 속성들에 대하여 고객들이 느끼는 긍정 또는 부정의 정도를 계산할 수 있지만, 그 분석능력이 단순하다는 한계가 있다. 본 논문에서는 온라인 고객리뷰들에 대하여 다차원적으로 분석할 수 있는 기법을 제안하였다. 기존의 OLAP기법을 텍스트 데이터형에 적용할 수 있도록 수정하였다. 다차원 분석모델은 명사축과 형용사축, 문서축으로 구성되는 3차원 공간 개념을 4개의 관계형 테이블로 실체화 한 것이다. 다차원 분석모델은 기존의 오피니언마이닝, 정보요약, 클러스터링 알고리즘들을 융합할 수 있는 새로운 틀이라는 점에서 그 가치가 있다. 본 논문에서 제안한 다차원 분석모델과 알고리즘들을 실제로 구현하여 온라인 고객리뷰에 대한 복잡한 분석을 수행할 수 있음을 확인하였다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제4권12호
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• pp.537-542
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• 2015

소프트웨어 공학에서 요구사항 분석은 전체 시스템의 성공률을 좌우한다. 요구사항에서 발생되는 오류는 전체 시스템에 영향을 주게 되고, 그 결과 고객의 만족도가 낮아진다. 따라서 요구사항 단계에서 정확한 분석을 위하여 이해관계자 간의 의견을 교환하고 수정할 수 있는 도구가 필요하게 된다. 본 논문에서는 이와 같은 문제를 해결하기 위하여 이해관계자 간의 의견을 교환할 수 있는 도구를 설계하고자 한다.

• Asian Journal for Public Opinion Research
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• 제10권4호
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• pp.293-315
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• 2022

Health care has become a rapidly growing industry where the role of nurses as a group of emotional labor employees is central and prone to burnout. The purpose of this study was to examine the role of supervision in moderating burnout caused by the effect of work intensity, customer contact, and self-efficacy, where the moderating role of supervision on burnout with its various predictors is still unstable. This quantitative study was based on research samples collected through questionnaires from 131 hospital nurses spread over two different locations. The questionnaire asked about supervision, work intensity, customer contact, self-efficacy and burnout used a Likert scale, which was then analyzed using SEM-PLS. The results indicated that work intensity and self-efficacy had a significant effect on burnout, while customer contact had no significant effect on burnout. Supervision as a moderator only significantly moderates the effect of work intensity on burnout, while supervision is not significant as a moderating variable on the effect of customer contact and self-efficacy on burnout. This study can contribute to the development of theories about burnout and practically can be used as a reference by policy makers in enhancing the role of supervision for nurses in hospitals.