• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• KIEE International Transactions on Power Engineering
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• v.4A no.1
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• pp.6-10
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• 2004

This paper presents an assessment of the customer interruption costs using a macro economic methodology of Korean customers by cities and provinces. The customer interruption cost is considered a very useful index in quantifying reliability worth from a customer point of view. This paper reviews the methodology to evaluate the customer interruption costs and ratio to the average revenues per electric energy sold for public, service agriculture, fishery, mining, manufacturing and residential sectors by cities and provinces in Korea.

• Proceedings of the Korea Database Society Conference
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• 2001.06a
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• pp.235-248
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• 2001

o Global Support Centers o 50 Worldwide Field Service Offices o Executive Sponsorship o Quarterly Customer Satisfaction Reviews o Compensation Plans Based on Customer Satisfaction (omitted)

• Korea Journal of Hospital Management
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• v.27 no.3
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• pp.1-14
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• 2022

Purposes: This purpose of this study was to check how much the online word of mouth influences on customer's hospital selection according to Kano's model. Methodology: Kano classified the attributes that affect customer's satisfaction into attractive, one-dimensional, indifferent, must-be, and reverse attributes. Among them, attractive and one-dimensional attributes make up the largest portion in hospital selection. Based on this, the influence of positive or negative online reviews on the selection of hospitals was investigated. Differentiated service was selected as the attractive attributes, and a kind, sufficient explanation was selected as the one-dimensional attributes. Then a questionnaire was conducted how much the positive or negative online reviews influence on hospital selection, respectively. It was conducted from August 7 to September 7, 2021 for medical consumers in their 20s and older who have used medical services for the past 3 years, and the final 142 questionnaires were analyzed. All data was analyzed by chi-square and two-way ANOVA using SPSS ver 25.0. Findings: The results showed that, in one-dimensional attributes, the difference between positive and negative reviews was not statistically significant, but in attractive attributes, positive and negative reviews showed a statistically significant difference. It suggests that positive reviews on attractive attributes had a greater influence on hospital selection. In terms of hospital selection, when the experimental participants were exposed to the positive reviews, the hospital selection ratio did not differ by Kano's attributes, but to the negative reviews it differed. The hospital selection ratio, even after they were exposed to negative reviews, was higher in the attractive attributes than in the one-dimensional attributes. Practical Implication: This study confirmed that hospital selection is influenced differently depending on the Kano's attributes and the direction of the reviews, and suggests that marketers should respond differently to each Kano's attributes when they deal with online reviews of hospitals.

• Asia pacific journal of information systems
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• v.33 no.2
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• pp.261-281
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• 2023

The proliferation of online customer reviews has completely changed how consumers purchase. Consumers now heavily depend on authentic experiences shared by previous customers. However, deceptive reviews that aim to manipulate customer decision-making to promote or defame a product or service pose a risk to businesses and buyers. The studies investigating consumer perception of deceptive reviews found that one of the important cues is based on review content. This study aims to investigate the impact of the information amount of review on the review truthfulness. This study adopted the Information Manipulation Theory (IMT) as an overarching theory, which asserts that the violations of one or more of the Gricean maxim are deceptive behaviors. It is regarded as a quantity violation if the required information amount is not delivered or more information is delivered; that is an attempt at deception. A topic modeling algorithm is implemented to reveal the distribution of each topic embedded in a text. This study measures information amount as topic diversity based on the results of topic modeling, and topic diversity shows how heterogeneous a text review is. Two datasets of restaurant reviews on Yelp.com, which have Filtered (deceptive) and Unfiltered (genuine) reviews, were used to test the hypotheses. Reviews that contain more diverse topics tend to be truthful. However, excessive topic diversity produces an inverted U-shaped relationship with truthfulness. Moreover, we find an interaction effect between topic diversity and reviews' ratings. This result suggests that the impact of topic diversity is strengthened when deceptive reviews have lower ratings. This study contributes to the existing literature on IMT by building the connection between topic diversity in a review and its truthfulness. In addition, the empirical results show that topic diversity is a reliable measure for gauging information amount of reviews.

• 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.

• Asia Marketing Journal
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• v.10 no.2
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• pp.125-151
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• 2008

Both companies and consumers are highly interested in on-line customer reviews which enable consumers to share their experience and knowledge about products. In this study, after classifying real reviews into context units and deriving categories, we analyzed differences between categories based on channel(manufacturers' homepage/ shopping mall), product attribute(search/experience) and price(high/low). The method to derive categories is based on roughly adopting constructs of ACSI model and elaborate and repetitive classification of real reviews. We set up the classification category with 3 levels. Level 1 consists of product and service, level 2 consists of function, design, price, purchase motive, suggestion/user-tip and recommendation/repurchase in product and AS/up-grade and delivery/others in service and level 3 is composed of details of level 2 of category. We could find remarkable differences between channels in all 8 items of level 2 of category. As the number of context units in homepage is more than in shopping mall, we found reviews in homepage is more concrete. Moreover, overall satisfaction in review was higher at homepage's. Also, in product attribute dimension, we found different patterns of reviews in design, purchase motive, suggestion/user-tip, recommendation/repurchase, AS/up-grade and delivery/others and no difference in overall customer's satisfaction. In price dimension, we found differences between high and low price in design, price and AS/up-grade and no difference in overall customer's satisfaction.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.19-33
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• 2009

With the continuously increasing volume of e-commerce transactions, it is now popular to buy some products and to evaluate them on the World Wide Web. The product reviews are very useful to customers because they can make better decisions based on the indirect experiences obtainable through the reviews. Product Reviews are results expressing customer's sentiments and thus are divided into positive reviews and negative ones. However, as the number of reviews in on-line shopping increases, it is inefficient or sometimes impossible for users to read all the relevant review documents. In this paper, we present a sentiment analysis algorithm for automatically classifying subjective opinions of customer's reviews using opinion mining technology. The proposed algorithm is to focus on product reviews of on-line shopping, and provides summarized results from large product review data by determining whether they are positive or negative. Additionally, this paper introduces an automatic review analysis system implemented based on the proposed algorithm, and also present the experiment results for verifying the efficiency of the algorithm.

• Journal of the Korea Convergence Society
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• v.6 no.4
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• pp.205-212
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• 2015

This paper aims to investigate key factors affecting customer adoption of the online review from the three perspectives such as customer review characteristics, reviewer characteristics, and customer characteristics. We collected data on customers who have experience in purchasing products in Taobao. The major findings are as follows. First, the customer review amount and vividness are not directly related to customer adoption of the online review. Second, the trust of reviewer and perceived similarity have positive effects on customer adoption of the online review. Third, the prior knowledge and product involvement increase customer adoption of the online review. Finally, customers' purchase intention is greatly determined by customer adoption of the online review. This paper presents the importance of the management of customer reviews and management method for the stakeholders of shopping mall to advancing Chinese market.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.863-876
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• 2022

Purpose: The purpose of this study is to propose the product quality satisfaction index based on multiple linear regression using customer reviews. Methods: The proposed framework is composed of four steps. First, we collect online reviews and divide it into insight phrases. The insight phrases are classified using product attribute dictionary and sentiment analysis is conducted. Second, the importance of attributes is calculated in consideration of both regression coefficient and frequency. Third, the positive rate is calculated concerning sentiment analysis result. Therefore, the quality satisfaction index is measured by the weighted sum of importance and positive rate in the last step. Results: We conduct a case study using 2-years(2020, 2021) of Samsung TV reviews to confirm the effectiveness of the proposed methodology. As a result, we found that Picture quality is the most crucial attribute in TV evaluation. The importance of Gaming and content has grown up as the positive rate has also increased. Therefore, the overall satisfaction of TV has increased in 2021 compared to 2020. Conclusion: The result of this study shows that the proposed index reveals the customer's mind efficiently and can be explained by the importance and positive rate of each attribute. By using the proposed index, companies are able to improve and the priority of improvement can be determined.