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

• Asia pacific journal of information systems
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• v.20 no.3
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• pp.33-49
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• 2010

We empirically examine the impact of online auction knowledge and category-specific knowledge on the final price of online auctions. Specifically, we question how the relationship between buying and selling experiences affects the final prices of online auctions. Related to the trust between buyers and sellers, we examine the multiple interactions between a buyer-seller pairand aim to identify how these repeated transactions influence the final price. To contrast these effects with other product related factors, we focus on so called 'common value' auctions of vintage stamps on eBay, in which the ex-post value of the product is the same among participating agents’ perceived value. Online auction of stamps provides a representative setting to examine the relationship between market experience and the auction participation behavior in the common value auction, as it provides the book value of stamp as well as price variation across individual buyers with different expertise levels. Our analysis of over 3000 stamps auctions on eBay indicates a significantly high frequency of buyer-seller (pair) interactions, thus suggesting a 'relationship view' of auctions. The work validates five hypotheses derived from the existing theory in economics, marketing, and information systems. Through the common-value auction data, we find that seller's online auction experience and category-specific experience favor sellers by increasing the final price. However, buyer's online auction experience does not affect the final price, but buyer's category-specific experience favors buyers by decreasing the final price. We find that the trust between two trading parties increases the final price.

• Korean Journal of Computational Design and Engineering
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• v.4 no.4
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• pp.371-380
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• 1999

This study proposes a process information model which can integrate various activities during the product development process, the system which can manage the process. Development process information will allow designers and managers to access previous tasks readily and utilize the information in making decisions at hand. While developing products in heterogeneous and distributed environments, concurrent and collaborative designs can be achieved by STEP. The process model in this study divides the task of product development into two fundamental parts, Specific Process(SP) and Normal Process(NP). SP includes the specific information and refer to those defined by prior STEP. NP is routine process excluded from being defined as SP. Due data information can be added to manage development pace and to show delayed tasks. As two or more different processes can be linked to show the entire development flow, Static STEP information can be dynamically interlaced. Remote location operations can be incorporated on the Internet using STEP, and information can be shared. The system has been built upon a process model schema so that task stage, design history, and different development style can be managed and accessed for each component. The system has been proven efficient in information management and exchange by several scenario testings.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.3 s.151
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• pp.470-480
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• 2006

Consumer may perceive needs of certain product information online rather than the number of pieces of information available for decision making. In addition, consumers may seek information that is more relevant to apparel category. Moreover, involved consumers intensify information seeking and seek certain information. The purpose of this study is to identify the perceived importance weight of each information cue when shopping apparel via the Internet, to investigate the differences of the perceived importance weight of product information cues in product category, and to examine the relationship between apparel involvement and the perceived importance weight of product information cues. This study employed a single-factor within-subjects design experiment that simulated online purchase situation for two product types, Jeans and a Shirt. A total of 125 college students participated in this study. Results indicated that selected information such size available, price, style, color description, item measurement, stock available, and item quality (in sequence) can be considered as global cues to judge product quality and influence purchase decision regardless of product category. The significant multivariate effects for product category on the perceived importance weight of product information cues were found. Personalization, fiber content, and fabric structure were product specific information cues. Consumers' product involvement significantly influenced the perception of information weight. Therefore, product information can be personalized based on consumer involvement

• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.358-369
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• 2021

This study aims to classify the dimensions of the sales information of secondhand clothing goods on the C2C secondhand trading platform and to systematically analyze the components of each dimension. To this end, the NVivo 12.0 qualitative data analysis software was used. The content analysis showed that the sales information of secondhand clothing goods was classified into four dimensions: detailed information of the sale goods, information specific to secondhand clothing goods, seller opinion information, and service information. The components of each dimension were as follows. The detailed information of the sale goods included size, sale price, item, design, brand name, material, color, wearing season, fit, gender, etc. The information specific to secondhand clothing goods included the number of times the item was worn, its purchase history, and product condition. Seller opinion information included product review, sales motivation, notes for the transaction, coordination proposal, and usage proposal. The service information included the transaction mode, exchange·return·refund, and promotion. The frequency analysis showed that the highest frequencies were sale goods(37.47%), information specific to secondhand clothing goods(24.63%), seller opinion information(20.54%), and service information(17.37%). This study will help C2C secondhand trading platform managers or sellers establish clear standards for presenting sales information and developing ideas toward constructing differentiated platform contents.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.254-262
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• 2009

As the trend of e-business activities develop, customers come into contact with products through on-line shopping sites and lots of customers refer product reviews before the purchasing on-line. However, as the volume of product reviews grow, it takes a great deal of time and effort for customers to read and evaluate voluminous product reviews. Lately, attention is being paid to Opinion Mining(OM) as one of the effective solutions to this problem. In this paper, we propose an efficient method for opinion sentiment classification of product reviews using product specific context information of words occurred in the reviews. We define the context information of words and propose the application of context for sentiment classification and we show the performance of our method through the experiments. Additionally, in case of word corpus construction, we propose the method to construct word corpus automatically using the review texts and review scores in order to prevent traditional manual process. In consequence, we can easily get exact sentiment polarities of opinion words in product reviews.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2093-2096
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• 2003

Trusted operating systems (OS) provide the basic security mechanisms and services that allow a computer system to protect, distinguish, and separate classified data. Trusted operating systems have been developed since the early 1980s and began to receive National Security Agency (NSA) evaluation in 1984. The researches about trusted OS are proceeding over the world, and new product type using the loadable security kernel module (LSKM) or dynamic link library (DLL) is being developed. This paper proposes a special type of product using LSKM and specific conditions for operational environment should be assumed.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.11
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• pp.1369-1380
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• 2005

This study suggests a strategy of providing apparel product information from the wearers' perspective on Internet shopping malls, as a way of compensating for the lack of opportunities to try on the actual product. On an actual Internet shopping mall that sells apparel product, the 'trial-experience information' (the experiential information provided by the fit models who tried on the products) was provided for 83 different items from four women's wear brands. The traffic and sales performances (number of visitors, page view, gross sales of goods, conversion rate, and the numbers of customer transactions) of the apparel product websites that contain trial-experience information were compared to the performances of the brand's websites before this information was implemented. The changes in percent contribution of these brands in women's wear category were also noted. The specific results are as follows: First, all the four performance measures as well as the percent contribution of the experiment products in women's wear category increased noticeably throughout the experiment period (11 to $103\%$). Second, when the percent contribution of these brands in women's wear category in terms of traffic and sales performances were compared to the previous year, these measures increased between $497\%\;and\;2851\%$. Third, the amount of customer transactions also increased after the trial-information was provided, yet to a relatively smaller extent $(29.04\%\;to\;55.25\%)$. The findings showed that trial-experience information provided on the Internet shopping malls may reduce customers' risk perception and lead to increased sales of apparel product and improve the site use ratio.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.838-855
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• 2019

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.131-137
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• 2018

People write reviews of numerous products or services on the Internet, in their blogs or community bulletin boards. These unstructured data contain important emotions and opinions about the author's product or service, which can provide important information for future product design or marketing. However, this text-based information cannot be evaluated quantitatively, and thus they are difficult to apply to mathematical models or optimization problems for product design and improvement. Therefore, this study proposes a method to quantitatively extract user's opinion or preference about a specific product or service by utilizing a lot of text-based information existing on the Internet or online. The extracted unstructured text information is decomposed into basic unit words, and positive rate is evaluated by using existing emotional dictionaries and additional lists proposed in this study. This can be a way to effectively utilize unstructured text data, which is being generated and stored in vast quantities, in product or service design. Finally, to verify the effectiveness of the proposed method, a case study was conducted using movie review data retrieved from a portal website. By comparing the positive rates calculated by the proposed framework with user ratings for movies, a guideline on text mining based evaluation of unstructured data is provided.