• The Journal of Information Systems
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• v.28 no.1
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• pp.1-16
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• 2019

Purpose The purpose of this study is to analyze causal relationship between quantity and quality for deriving ranking model of Online reviews. Thus, we propose implications for deriving the ranking model for retrieving Online reviews more effectively. Design/methodology/approach We collected Online review from Tripadvisor web sites which might be a kind of world-famous tourism web sites. We transformed the natural text reviews to quantified data which consists of quantified positive opinions, quantified negative opinions, quantified modification opinions, reviews lengths and grade scores by using opinion mining technologies in R package. We executed corelation and regression analysis about the data. Findings According to the empirical analysis result, this study confirmed that the review length influenced positive opinion, negative opinion and modification opinion. We also confirmed that negative opinion and modification opinion influenced the grade score.

• 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.

• Review of Korea Contents Association
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• v.15 no.3
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• pp.40-47
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• 2017

• Korean Medical Education Review
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• v.22 no.2
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• pp.144-144
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• 2020

• Journal of Information Science Theory and Practice
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• v.12 no.1
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• pp.87-101
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• 2024

As the academic publishing environment evolves rapidly and the open science paradigm emerges, the demand for efficient and transparent peer review is growing. This study outlines efforts to actively introduce advanced concepts in scholarly communication into the submission and review system. AccessON Peer Review Management System Plus (ACOMS+), developed and operated by the Korea Institute of Science and Technology Information, is an online submission and peer review system that aims for open science. This study provides an overview of ACOMS+ and presents its four main features: open peer review, open access publishing and self-archiving, online quantitative/qualitative evaluation, and peer reviewer invitation. The directions for further developing ACOMS+ to fully support open science are also discussed. ACOMS+ is the first system in Korea to introduce the open peer review process and is distinguished as a system that supports open access publishing and digital transformation of academic journals. Furthermore, ACOMS+ is expected to contribute to the advancement of the academic publishing environment through the increasing shift toward open access publishing, transparent peer review, and open science.

• Journal of Information Technology Services
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• v.13 no.1
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• pp.43-55
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• 2014

More and more people are gravitating to reading products reviews prior to making purchasing decisions. As a number of reviews that vary in usefulness are posted every day, much attention is being paid to measuring their helpfulness. The goal of this paper is to investigate firstly various determinants of the helpfulness of reviews, and intends to examine the moderating effect of product type, i.e., search or experience goods on the product sales, helpfulness and helpfulness votes of online reviews. The determinants include product data, review characteristics, and textual characteristics of reviews. The results indicate that the direct effect exists for the determinants of product sales, helpfulness, and helpfulness votes. Further, the moderating effects of product type exist for these determinants on three dependent variables. The results of study will identify helpful online review and design review sites effectively.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.219-226
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• 2016

In this paper, we propose an efficient repository model for online software education. The software education of app development consists of 7 stages: coding & debugging, submit, collaboration, review, validate, deployment, certification. Proposed repository model supports all 7 stages efficiently. In the coding & debugging stage, the students repeat coding and debugging of source. In the submit stage, the output of previous process such as source codes, project, and videos, are uploaded to repository server. In the collaboration stage, other students or experts can optimize or upgrade version of source code, project, and videos stored in the repository. In the review stage, mentors can review and send feedbacks to students. In the validate stage, the specialists validate the source code, project, and the videos. In the deployment stage, the verified source code, project, and videos are deployed. In the certification stage, the source code, project, and the videos are evaluated to issue the certificate.

• Journal of Korea Game Society
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• v.17 no.3
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• pp.33-44
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• 2017

With the development of the Internet environment, various types of online reviews are being generated and exchanged among consumers to share their opinions. In line with this trend, companies are making efforts to analyze online reviews and use the results in various business activities such as marketing, sales, and product development. However, research on online review in industry related to 'Video Game' which is representative experience goods has not been performed enough. Therefore, this study analyzed STEAM community review data using machine learning techniques. We analyzed the factors affecting the opinion of other users' game review. We also propose managerial implications to incease user loyalty and usability.

• Asia Marketing Journal
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• v.24 no.1
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• pp.13-28
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• 2022

Despite the benefits of online shopping, we easily observe consumer behaviour when making purchases through offline channels. Why do they choose to go offline by taking the effort to go there? As a factor influencing decision-making, this study assumes that distrust of online shopping increases webrooming intentions that online consumers move to offline channels. Consumers check online reviews as well as seller information to increase their purchasing confidence. There are few studies on the effect of negative online reviews on consumers' purchasing decisions. Contrary to the pessimistic results of previous studies, the results of this study explain the mechanism by which consumers who saw negative online reviews feel distrust of online shopping and go to offline stores. It provides implications for understanding the migration phenomenon of online shoppers to offline channels and what strategies should be prepared to retain and attract customers to each channel.