• The Journal of Information Systems
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• v.32 no.3
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• pp.133-150
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• 2023

Purpose This study aims to develop classification models using a decision tree algorithm to identify core keywords and rules influencing online consumer review evaluations for the robot vacuum cleaner on Amazon.com. The difference from previous studies is that we analyze core keywords that affect the evaluation results by dividing the subjects that evaluate online consumer reviews into self-evaluation (star ratings) and peer evaluation (helpfulness votes). We investigate whether the core keywords influencing star ratings and helpfulness votes vary across different products and whether there is a similarity in the core keywords related to star ratings or helpfulness votes across all products. Design/methodology/approach We used random under-sampling to balance the dataset. We progressively removed independent variables based on decreasing importance through backwards elimination to evaluate the classification model's performance. As a result, we identified classification models that best predict star ratings and helpfulness votes for each product's online consumer reviews. Findings We have identified that the core keywords influencing self-evaluation and peer evaluation vary across different products, and even for the same model or features, the core keywords are not consistent. Therefore, companies' producers and marketing managers need to analyze the core keywords of each product to highlight the advantages and prepare customized strategies that compensate for the shortcomings.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.876-883
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• 2008

A clearly defined e-catalog (or product) information is a key foundation for an e-commerce system. A classification (or categorization) is a core information to build clear e-catalogs, can play an important role in quality of e-commerce systems using e-catalogs. However, as the wide use of online business transactions, the volume of e-catalog information that needs to be managed in a system has become drastically large, and the classification task of such data has become highly complex. In this paper, we present an e-catalog classifier system, and report on our effort to improve an e-catalog management process and to standardize e-catalogs for enterprises by use of automated approach for e-catalog classifier systems. Also we introduce some of the issues that we have experienced in the projects, so that our work may help those who do a similar project in the future.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1031-1040
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• 2004

Electrocardiogram being the recording of the heart's electrical activity provides valuable clinical information about heart's status. Many re-searches have been pursued for heart disease diagnosis using ECG so far. However, electrocardio-graph uses foreign diagnosis algorithm due to inaccuracy of diagnosis results for a heart disease. This paper suggests ECG data collection, data preprocessing and heart disease pattern classification using data mining. This classification technique is the FB(Frequent pattern Bayesian) classifier and is a combination of two data mining problems, naive bayesian and frequent pattern mining. FB uses Product Approximation construction that uses the discovered frequent patterns. Therefore, this method overcomes weakness of naive bayesian which makes the assumption of class conditional independence.

• Journal of the Korean Dietetic Association
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• v.23 no.4
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• pp.453-463
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• 2017

The purpose of this study was to standardize ingredients used by school foodservices. This study analyzed the current notation of ingredients in used by used in school foodservices through the NEIS system employed by school foodservices of elementary schools through high schools in South Korea. Specifically, this study suggests systemized standardization of ingredient classification and quality attributes of at school foodservices by applying a case study analysis. The findings from the case analysis of the Electronic Procurement System operator are as follows. Classifications for ingredients of the NEIS system used by school food services consisted of included food group, food name, detailed food name, and description. Classification was not clearly divided between the classification scheme and the attribute system. Therefore, food group, food name, and product information of each food should be categorized as the classification scheme, whereas the detailed food name (excluding product information) and description should be standardized as the attribute system, which is composed of required attributes, recommended attributes, and other attributes. This study suggests that system standardization should be carried out in the field of school foodservices, as advancements between distributors and school food service providers could affect food ingredient quality. Thus, standardization can influence purchase and distribution in many ways.

• Archives of design research
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• v.16 no.3
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• pp.81-90
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• 2003

The purpose of the study is to propose a new way of classification for products and to forecast the future of products through the physical factor and the mental factor as human natures. For the purpose of the study, the research was carried out in three ways. Firstly, the study considered the evolutional process of products through human natures. At this stage, the study defined that the physical ability and the mental ability of human are the cores of the product's evolution. Secondly, for understanding human evolution, the study set up two types of future humans . Finally, the study classified products by the physical factor and the mental factor as human natures with the aspect of embryology. As the results, the study illustrated two different species of products and their futures.

• The Journal of Society for e-Business Studies
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• v.8 no.1
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• pp.121-139
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• 2003

Currently, it is not popular to use the STEP based product information in the construction drawing files, in spite of the importance and possibility of using various product data in drawing files on the CALS/EC environment. This paper aims to demonstrate a construction drawing information management system based on ISO 10303/STEP. To achieve this aim, the authors have analyzed the current construction drawing information classification hierarchy widely used for domestic and international, and examined the material data connection mechanism within CAD drawing data, and finally investigated the management systems for construction documentations and drawings in a public companies. Therefore, the expected benefit of the proposed system is that STEP drawing information management will be done standardization and the information of STEP construction drawing can be managed, shared and supported design business through materials data connection.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.295-303
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• 2005

Semantic Web and its related technologies have been opening the era of information sharing via Web. In the meantime, there are several huddles to overcome toward the new era and one of the major huddles is information integration issue unless we build and use a single unified but huge ontology which address everything in the world. Particularly in e-business area, information integration problem must be a great concern in search and comparison of products from various internet shopping sites and e-marketplaces. To overcome such an information integration problem, we propose an ontology driven mapping algorithm between heterogeneous product classification and description frameworks. We also perform comparative evaluation of the proposed mapping algorithm against a well-known ontology mapping tool, PROMPT.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.4
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• pp.91-99
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• 2006

A POP system, which collects manufacturing data from the shop floors and supply them to higher level systems, should be maintained and upgraded according to the change of production environment such as new product introduction. This situation leads to the need of a cost-effective system development methodology. In this paper, a methodology based on the classification and the similarity comparison of manufacturing processes is proposed. In this, a new product is classified according to the similarity of its manufacturing processes, which enables recycling of existing system modules. The proposed methodology has been tested in the case of an electronics parts manufacturing company, where a POP system is implemented. The result shows that the proposed methodology can save time and efforts for system implementation.

• Journal of Korea Multimedia Society
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• v.22 no.6
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• pp.665-675
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• 2019

In this paper, we compare and analyze the classification performance of deep learning algorithm Convolutional Neural Network(CNN) ac cording to ensemble generation and combining techniques. We used several CNN models(VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, GoogLeNet) to create 10 ensemble generation combinations and applied 6 combine techniques(average, weighted average, maximum, minimum, median, product) to the optimal combination. Experimental results, DenseNet169-VGG16-GoogLeNet combination in ensemble generation, and the product rule in ensemble combination showed the best performance. Based on this, it was concluded that ensemble in different models of high benchmarking scores is another way to get good results.

• ETRI Journal
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• v.38 no.3
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• pp.494-501
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• 2016

Sentiment analysis incorporates natural language processing and artificial intelligence and has evolved as an important research area. Sentiment analysis on product reviews has been used in widespread applications to improve customer retention and business processes. In this paper, we propose a method for performing an intensified sentiment analysis on customer product reviews. The method involves the extraction of two feature sets from each of the given customer product reviews, a set of acoustic features (representing emotions) and a set of lexical features (representing sentiments). These sets are then combined and used in a supervised classifier to predict the sentiments of customers. We use an audio speech dataset prepared from Amazon product reviews and downloaded from the YouTube portal for the purposes of our experimental evaluations.