• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.272-284
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• 2009

In this paper, we proposed the methodology for analyzing online customer reviews by using text mining technologies. We introduced marketing segmentation into the methodology because it would be efficient and effective to analyze the online customers by grouping them into similar online customers that might include similar opinions and experiences of the customers. That is, the methodology uses categorization and information extraction functions among text mining technologies, matched up with the concept of market segmentation. In particular, the methodology also uses cross-tabulations analysis function which is a kind of traditional statistics analysis functions to derive rigorous results of the analysis. In order to confirm the validity of the methodology, we actually analyzed online customer reviews related with tourism by using the methodology.

• The Journal of the Korea Contents Association
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• v.10 no.11
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• pp.30-37
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• 2010

It is very important to analyze online customer reviews, which are small documents of writing opinions or experiences about products or services, for both customers and companies because the customers can get good informations and the companies can establish good marketing strategies. In this paper, we did not propose only dependency network model which is tool for analyzing online customer reviews, but also designed and implemented the system based on the dependency network model. The dependency network model analyzes both subjective and objective sentences, so that it can represent relative importance and relationship between the nouns in the sentences. In the result of implementing, we recognized that relative importance and relationship between the features of products or services, which can not be mined by opinion mining, can be represented by the dependency network model.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.21-36
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• 2022

Recently, as COVID-19 has made the e-commerce market expand widely, customers who have different consumption patterns appear in the market. Because companies can obtain opinions and information of customers from reviews, they increasingly face the requirements of managing customer reviews on online platform. In this study, we analyze customers and carry out market segmentation for classifying and defining type of customers in e-commerce. Specifically, K-means clustering was conducted on customer review data collected from Wemakeprice online shopping platform, which leads to the result that six clusters were derived. Finally, we define the characteristics of each cluster and propose a customer management plan. This paper is possible to be used as materials which identify types of customers and it can reduce the cost of customer management and make a profit for online platforms.

• Knowledge Management Research
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• v.24 no.4
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• pp.153-176
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• 2023

Generative AI, especially conversational AI like ChatGPT, has recently gained traction as a technological alternative for automating customer service. However, there is still a lack of research on whether current generative AI technologies can effectively replace traditional human managers in customer service automation, and whether they are advantageous in some situations and disadvantageous in others, depending on the conditions and environment. To answer the question, "Can generative AI replace human managers in customer service activities?", this study conducted experiments and surveys on customer online reviews of a food delivery platform. We applied the perspective of the elaboration likelihood model to generate hypotheses about whether there is a difference between positive and negative online reviews, and analyzed whether the hypotheses were supported. The analysis results indicate that for positive reviews, generative AI can effectively replace human managers. However, for negative reviews, complete replacement is challenging, and human managerial intervention is considered more desirable. The results of this study can provide valuable practical insights for organizations looking to automate customer service using generative AI.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.43-61
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• 2016

Customer reviews help potential customers make purchasing decisions. However, the prevalence of reviews on websites push the customer to sift through them and change the focus from a mere search to identifying which of the available reviews are valuable and useful for the purchasing decision at hand. To identify useful reviews, websites have developed different mechanisms to give customers options when evaluating existing reviews. Websites allow users to rate the usefulness of a customer review as helpful or not. Amazon.com uses a ratio-type helpfulness, while Yelp.com uses a count-type usefulness index. This usefulness index provides helpful reviews to future potential purchasers. This study investigated the effects of sentiment and readability on useful votes for customer reviews. Similar studies on the relationship between sentiment and readability have focused on the ratio-type usefulness index utilized by websites such as Amazon.com. In this study, Yelp.com's count-type usefulness index for restaurant reviews was used to investigate the relationship between sentiment/readability and usefulness votes. Yelp.com's online customer reviews for stores in the beverage and food categories were used for the analysis. In total, 170,294 reviews containing information on a store's reputation and popularity were used. The control variables were the review length, store reputation, and popularity; the independent variables were the sentiment and readability, while the dependent variable was the number of helpful votes. The review rating is the moderating variable for the review sentiment and readability. The length is the number of characters in a review. The popularity is the number of reviews for a store, and the reputation is the general average rating of all reviews for a store. The readability of a review was calculated with the Coleman-Liau index. The sentiment is a positivity score for the review as calculated by SentiWordNet. The review rating is a preference score selected from 1 to 5 (stars) by the review author. The dependent variable (i.e., usefulness votes) used in this study is a count variable. Therefore, the Poisson regression model, which is commonly used to account for the discrete and nonnegative nature of count data, was applied in the analyses. The increase in helpful votes was assumed to follow a Poisson distribution. Because the Poisson model assumes an equal mean and variance and the data were over-dispersed, a negative binomial distribution model that allows for over-dispersion of the count variable was used for the estimation. Zero-inflated negative binomial regression was used to model count variables with excessive zeros and over-dispersed count outcome variables. With this model, the excess zeros were assumed to be generated through a separate process from the count values and therefore should be modeled as independently as possible. The results showed that positive sentiment had a negative effect on gaining useful votes for positive reviews but no significant effect on negative reviews. Poor readability had a negative effect on gaining useful votes and was not moderated by the review star ratings. These findings yield considerable managerial implications. The results are helpful for online websites when analyzing their review guidelines and identifying useful reviews for their business. Based on this study, positive reviews are not necessarily helpful; therefore, restaurants should consider which type of positive review is helpful for their business. Second, this study is beneficial for businesses and website designers in creating review mechanisms to know which type of reviews to highlight on their websites and which type of reviews can be beneficial to the business. Moreover, this study highlights the review systems employed by websites to allow their customers to post rating reviews.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.207-218
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• 2020

Recently, due to the increase of online reviews and the development of analysis technology, the interest and demand for online review analysis continues to increase. However, previous studies have not considered the emotions contained in each vocabulary may differ from one reviewer to another. Therefore, this study first classifies the customer group according to the customer's grade, and presents the result of analyzing the difference by performing review analysis for each customer group. We found that the price factor had a significant influence on the evaluation of products for customers with high ratings. On the contrary, in the case of low-grade customers, the degree of correspondence between the contents introduced in the mall and the actual product significantly influenced the evaluation of the product. We expect that the proposed methodology can be effectively used to establish differentiated marketing strategies by identifying factors that affect product evaluation by customer group.

• 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.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.47-54
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• 2022

The development of the Internet of Things led to new services that did not exist before. This required a change to the existing network. This study aims to verify the service quality, satisfaction, repurchase intention relationship, and the moderating effect of online reviews of Chinese consumers using fashion shopping malls. The results of the study showed that from the perspective of consumers in their 20s and 30s in China, the type, reliability, convenience, and interaction of service quality had a positive effect on customer satisfaction and repurchase intention. In addition, negative reviews among online reviews had a great influence on repurchase intention. Based on the results of the study, it will help improve the effect on online product reviews and in-depth understanding of the acceptance of online product reviews for online fashion shopping malls, and establish strategies for fashion companies to effectively manage online product reviews information.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Knowledge Management Research
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• v.23 no.1
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• pp.187-201
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• 2022

Online reviews written by tourists provide important information for the management and operation of the tourism industry. The star rating of online reviews is a simple quantitative evaluation of a product or service, but it is difficult to reflect the sincere attitude of tourists. There is also an issue; the star rating and review content are not matched. In this study, a star rating prediction model based on online review content was proposed to solve the discrepancy problem. We compared the differences in star ratings and sentiment by continent through sentiment analysis on tourist attractions and hotels written by foreign tourists who visited Korea. Variables were selected through TF-IDF vectorization and sentiment analysis results. Logit, artificial neural network, and SVM(Support Vector Machine) were used for the classification model, and artificial neural network and SVR(Support Vector regression) were applied for the rating prediction model. The online review rating prediction model proposed in this study could solve inconsistency problems and also could be applied even if when there is no star rating.