• Journal of Korean Society for Quality Management
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• v.47 no.1
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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.

• Korea Journal of Hospital Management
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• v.18 no.3
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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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• v.33 no.5
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.133-140
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• 2011

For both customers and companies, it is very important to analyze online customer reviews, which consist of small documents that include opinions or experiences about products or services, because the customers can get good informations and the companies can establish good marketing strategies. In this paper, we propose the association model for the opinion mining which can analyze customer opinions posted on web. The association model is to modify the association rules mining model in data mining in order to apply efficiently and effectively the association mining techniques to the opinion mining. We designed and implemented the opinion mining systems based on the modified association model and the grouping idea which would enable it to generate significant rules more.

• IE interfaces
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• v.12 no.4
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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.

• Annual Conference on Human and Language Technology
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• 2018.10a
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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.

• Information Systems Review
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• v.17 no.1
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• pp.49-64
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• 2015

Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.76-85
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• 2012

Through opinion mining, we can analyze the degree of positive or negative sentiments that customers feel about important entities or attributes in online customer reviews. But, the limit of the opinion mining techniques is to provide only simple functions in analyzing the reviews. In this paper, we proposed novel techniques that can analyze the online customer reviews multi-dimensionally. The novel technique is to modify the existing OLAP techniques so that they can be applied to text data. The novel technique, that is, multi-dimensional analytic model consists of noun, adjective and document axes which are converted into four relational tables in relational database. The multi-dimensional analysis model would be new framework which can converge the existing opinion mining, information summarization and clustering algorithms. In this paper, we implemented the multi-dimensional analysis model and algorithms. we recognized that the system would enable us to analyze the online customer reviews more complexly.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.12
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• pp.537-542
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• 2015

Project success is depend on requirement analysis for in the software engineering. Requirements error have a effect in the whole system. As a result, the customer satisfaction will deteriorate. Therefore, we are need to tool that stakeholder's opinion exchange and modify for a accurate analysis in the requirement phase. In this paper, we are design that tool of the stakeholder's opinion exchange.

• Asian Journal for Public Opinion Research
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• v.10 no.4
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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.