• Journal of Distribution Science
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• v.17 no.5
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• pp.85-94
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• 2019

Purpose - The purpose of this study is to analyze the impact of customer's communication on sales performance in the online market. Research design, data, and methodology - This study uses linear regression analysis to examine the effects of product review characteristics which are the result of customer's communication, on sales performance by using product reviews of online marketplace Amazon. Result - The increase in the number of product reviews positively affected sales performance. An increase in extreme opinions in the product review has a positive effect on sales performance. The product review length has a negative effect on sales performance. Conclusions - This study has shown the online marketplace customers' communication can influence sales performance using product review big data. This study contributed to the theoretical completeness by analyzing all the products of the book category in Amazon online market. This research will complement the theories regard to the customer behavior affecting sales performance. We expect the empirical analysis result will provide empirical help to sellers, online marketplace operators, and customers. In particular, the number of letters in the product may negatively affect sales performance, so sellers need to consider this effect carefully when exposing product reviews.

• Journal of Distribution Science
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• v.18 no.10
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• pp.65-75
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• 2020

Purpose: This study aims to examine how to review contents of experiential and utilitarian products (e.g., skincare products) and how to affect review helpfulness by applying natural language processing techniques. Research design, data, and methodology: This study uses 69,633 online reviews generated for the products registered at Amazon.com by 13 Korean cosmetic firms. The authors identify key topics that emerge about consumers' use of skincare products such as skin type and skin trouble, by applying bigram analysis. The review content variables are included in the review helpfulness model, including other important determinants. Results: The estimation results support the positive effect of review extremity and content on the helpfulness. In particular, the reviewer's skin type information was recognized as highly useful when presented together as a basis for high-rated reviews. Moreover, the content related to skin issues positively affects review helpfulness. Conclusions: The positive relationship between extreme reviews and helpfulness of reviews challenges the findings from prior literature. This result implies that an in-depth study of the effect of product types on review helpfulness is needed. Furthermore, a positive effect of review content on helpfulness suggests that applying big data analytics can provide meaningful customer insights in the online retail industry.

• The Journal of Information Systems
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• v.27 no.1
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• pp.89-110
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• 2018

Purpose There is much information in customer reviews, but finding key information in many texts is not easy. Business decision makers need a model to solve this problem. In this study we propose a multi-topic sentiment analysis approach using Latent Dirichlet Allocation (LDA) for user-generated contents (UGC). Design/methodology/approach In this paper, we collected a total of 104,039 hotel reviews in seven of the world's top tourist destinations from TripAdvisor (www.tripadvisor.com) and extracted 30 topics related to the hotel from all customer reviews using the LDA model. Six major dimensions (value, cleanliness, rooms, service, location, and sleep quality) were selected from the 30 extracted topics. To analyze data, we employed R language. Findings This study contributes to propose a lexicon-based sentiment analysis approach for the keywords-embedded sentences related to the six dimensions within a review. The performance of the proposed model was evaluated by comparing the sentiment analysis results of each topic with the real attribute ratings provided by the platform. The results show its outperformance, with a high ratio of accuracy and recall. Through our proposed model, it is expected to analyze the customers' sentiments over different topics for those reviews with an absence of the detailed attribute ratings.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.718-732
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• 2020

Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.85-97
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• 2021

Online consumer activities have increased considerably since the COVID-19 outbreak. For the products and services which have an impact on everyday life, online reviews and recommendations can play a significant role in consumer decision-making processes. Thus, to better serve their customers, online firms are required to build online-centric marketing strategies. Especially, it is essential to define core value of customers based on the online customer reviews and to propose these values to their customers. This study discovers specific perceived values of customers in regard to a certain product and service, using online customer reviews and proposes a customer value proposition methodology which enables online firms to develop more effective marketing strategies. In order to discover customers value, the methodology employs a text-mining technology, which combines a sentiment analysis and topic modeling. By the methodology, customer emotions and value factors can be more clearly defined. It is expected that online firms can better identify value elements of their respective customers, provide appropriate value propositions, and thus gain sustainable competitive advantage.

• The Journal of Information Systems
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• v.31 no.2
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• pp.1-18
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• 2022

Purpose Customer review is a major factor in choosing certain restaurants. This study investigates the key factors affecting customer's evaluation about restaurants. With the recent intensification of competition among restaurants in the service industry, the analysis results are expected to provide in-depth insights for enhancing customer experiences. Design/methodology/approach We collected information and reviews provided at the restaurants in the Kakao Map platform. The information collected is based on the information of 3,785 restaurants in Daegu registered on Kakao Map. Based on the information collected, seven independent variables, including number of rating registered, number of reviews, presence or absence of safe restaurants, presence or absence of a posting about holding facilities, presence or absence of a posting about business hours, presence or absence of a posting about hashtags, and presence or absence of break times, were used. Dependent variable is restaurant rating. Multiple regression between independent variables and restaurant rating was carried out. Findings The results of the study confirmed that number of rating registered, presence or absence of a posting about business hours, and presence or absence of a posting about hash tags have an positive effects on the restaurant rating. The number of reviews had a negative effect on the restaurant rating. In addition, in order to confirm the role of customer's reviews, we carried out LDA topic modeling. We divided the topics into the positive review and the negative reviews.

• Journal of Information Technology Services
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• v.20 no.5
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• pp.159-176
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• 2021

In this study, we performed a comparative analysis of the sentiment value for the tourists in USA, China, and Korea on the COVID19 pandemic era to explore and find out the features of the tourists by using online reviews. We collected a total of 243,826 online hotel reviews for metropolitan city and vacation spot in the three countries to compare the features between the business and the vacation trips. We collected the online reviews into the tow groups from Jan. 1, 2019 to Nov. 31, 2019 for before COVID19 pandemic and from Apr. 1, 2020 to Deb 28, 2021 for during COVID19. Online reviews were categorized into 6 dimensions using LDA model. Sentiment analysis were presented for 6 dimensions by utilizing a lexicon base. We proposed an approach to analyzing the importance of each attribute by applying 6-dimensional sentiment values to conjoint analysis. Our empirical analysis showed that the proposed approach could explore and find out the changed features of travelers during the COVID19 pandemic.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.24-39
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• 2022

For the success of the plant project, it is necessary to review the project's progress through technical and business reviews at an appropriate time, such as design and construction, and determine whether to invest or to proceed with the project to manage risks. In particular, since the plant development projects are not mass-produced, trial and error in design and construction can have severe impacts in terms of cost and schedule. To this end, gate reviews are currently being conducted in plants and other industrial sectors, but there are few studies on how to conduct gate reviews suitable for the plant field. In addition, there is little literature to refer to when conducting gate reviews. So, in this study, we present an overall framework that includes the types of gate reviews to be performed and items to be checked in each gate review on small and medium-sized plant development projects in which the owner directly develops and operates plants.

• The Journal of Industrial Distribution & Business
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• v.14 no.1
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• pp.67-74
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• 2023

Purpose: This study aims to investigate how the online review usefulness affect consumers' shopping behavior in non-face-to-face shopping, which is now very common format of shopping environment after COVID-19 pandemic. Factors influencing online reviews were determined as quantity of review, agreement of review and characteristic of review based on research by existing researchers. Research design, data, and methodology: Customers in their teens to 60s who had experience of checking online reviews and purchasing products were surveyed using a Google questionnaire form from January 15, 2022 to February 19, 2022. To verify the validity and reliability of the research model, confirmatory factor analysis and discriminant validity analysis were conducted. In addition, the causal relationship between factors was verified through path analysis. Results: As a result, quantity of review and agreement of review had a statistically significant effect on review usefulness. However, characteristic of review did not have a statistically significant influence on review usefulness. And review usefulness had a statistically significant effect on attitude and purchase intention. Conclusions: This study investigated the factors affecting usefulness of online reviews and empirically analyzed the effects of online reviews on consumer attitudes and purchase intentions providing practical and theoretical implications for corporate online review management.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.6
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• pp.1027-1037
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• 2023

This paper presents an efficient method for generating informative apparel fit comments by analyzing 122 fit reviews found in 7 menswear patternmaking textbooks, which include both domestic and foreign sources. The fit reviews for menswear were categorized into top and bottoms, and the expressions varied based on body parts, causes, and fit issue appearances. The causes of fit issues could be attributed to size errors and structural errors in both top and bottoms. Both top and bottoms had fit reviews concerning unique body types, but it could cause trouble among learners as both were based on unclear criteria for body type classification and lacked relevant explanations. Common fit issue appearances included compound wrinkles, pulling wrinkles, sagging wrinkles, and garment being away from the body. No clear correlation was observed between the causes of fit issues and specific appearances. Limitations were identified in using textbooks as educational data, such as inconsistent solutions for different body types or fit issues, and the presence of ambiguous visual materials. As a result, strategies such as categorizing fit issue appearances, providing 3D visual examples with subcategorized causes, body types and parts could enhance quality of fit reviews and improve fit outcomes in clothing production systems.