• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.05a
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• pp.49-50
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• 2011

• Health Policy and Management
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• v.16 no.2
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• pp.49-76
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• 2006

The aim of this study was to explore the effects of a computerized review program which was introduced in August 1, 2003, using claims data for acute respiratory infection related diseases. National Health Insurance (NHI) claims data on respiratory infection related diseases before and after the introduction, with six month intervals respectively, were used for the analysis. Clinic was the unit of observation, and clinics with only one physician whose specialty was internal medicine, pediatrics, otorhinolaryngology and family medicine and clinics with a general practitioner were selected. The final sample had 7,637 clinics in total. Indices used to measure practice pattern was prescription rates of antibiotics, prescription rates of injection drug per visit, treatment costs per claim, and total costs per claim. Changes in the number of claims for major disease categories and upcoding index for disease categories were used to measure claiming behavior. Data were analysed using descriptive analysis, t-test for indices changes before and after the introduction, analysis of variance (ANOVA) for practice pattern change for major disease categories, and multiple regression analysis to identify whether new system influenced on provider' practice patterns or not. Prescription of antibiotics, prescription rates of injection drug, treatment costs per claim, and total costs per claim decreased significantly. Results from multiple regression analysis showed that a computerized review system had effects on all the indices measuring behavior. Introduction of the new system had the spillover effects on the provider's behavior in the related disease categories in addition to the effects in the target diseases, but the magnitude of the effects were bigger among the target diseases. Rates of claims for computerized review over total claims for respiratory diseases significantly decreased after the introduction of a computerized review system and rates of claims for non target diseases increased, which was also statistically significant. Distribution of the number of claims by disease categories after the introduction of a computerized review system changed so as to increase the costs per claims. Analysis of upcoding index showed index for 'other acute lower respiratory infection (J20-22)', which was included in the review target, decreased and 'otitis media (H65, H66)', which was not included in the review target, increase. Factors affecting provider's practice patterns should be taken into consideration when policies on claims review method and behavior changes. It is critical to include strategies to decrease the variations among providers.

• The Journal of Information Systems
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• v.29 no.4
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• pp.171-191
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• 2020

Purpose The study aims to compare the online review writing behavior of users in China and the United States through text mining on online reviews' text content. In particular, existing studies have verified that there are differences in online reviews between different cultures. Therefore, the purpose of this study is to compare the differences between reviews written by Chinese and American tourists by analyzing text contents of online reviews based on cultural theory. Design/methodology/approach This study collected and analyzed online review data for hotels, targeting Chinese and US tourists who visited Korea. Then, we analyzed review data through text mining like sentiment analysis and topic modeling analysis method based on previous research analysis. Findings The results showed that Chinese tourists gave higher ratings and relatively less negative ratings than American tourists. And American tourists have more negative sentiments and emotions in writing online reviews than Chinese tourists. Also, through the analysis results using topic modeling, it was confirmed that Chinese tourists mentioned more topics about the hotel location, room, and price, while American tourists mentioned more topics about hotel service. American tourists also mention more topics about hotels than Chinese tourists, indicating that American tourists tend to provide more information through online reviews.

• Journal of Information Technology Services
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• v.15 no.2
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• pp.169-184
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• 2016

Smartphone use is rapidly spreading due to the advantage of being able to connect to the Internet anytime, anywhere--and mobile app development is developing accordingly. The characteristic of the mobile app market is the ability to launch one's app into foreign markets with ease as long as the platform is the same. However, a large amount of prior research asserts that consumers behave differently depending on their culture and, from this perspective, various studies comparing the differences between consumer behaviors in different countries exist. Accordingly, this research, which uses online product reviews (OPRs) in order to analyze the cultural differences in consumer behavior comparatively by nationality, proposes to compare the U.S. and South Korea by selecting ten apps which were released in both countries in order to perform a sentimental analysis on the basis of star ratings and, based on those ratings, to interpret the sentiments in reviews. This research was carried out to determine whether, on the basis of ratings analysis, analysis of review contents for sentiment differences, analysis of LDA topic modeling, and co-occurrence analysis, actual differences in online reviews in South Korea and the U.S. exist due to cultural differences. The results confirm that the sentiments of reviews for both countries appear to be more negative than those of star ratings. Furthermore, while no great differences in high-raking review topics between the U.S. and South Korea were revealed through topic modeling and co-occurrence analyses, numerous differences in sentiment appeared-confirming that Koreans evaluated the mobile apps' specialized functions, while Americans evaluated the mobile apps in their entirety. This research reveals that differences in sentiments regarding mobile app reviews due to cultural differences between Koreans and Americans can be seen through sentiment analysis and topic modeling, and, through co-occurrence analysis, that they were able to examine trends in review-writing for each country.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.650-664
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• 2022

Mobile applications can be easily downloaded and installed via markets. However, malware and malicious applications containing unwanted advertisements exist in these application markets. Therefore, smartphone users install applications with reference to the application review to avoid such malicious applications. An application review typically comprises contents for evaluation; however, a false review with a specific purpose can be included. Such false reviews are known as fake reviews, and they can be generated using artificial intelligence (AI)-based text-generating models. Recently, AI-based text-generating models have been developed rapidly and demonstrate high-quality generated texts. Herein, we analyze the features of fake reviews generated from Generative Pre-Training-2 (GPT-2), an AI-based text-generating model and create a model to detect those fake reviews. First, we collect a real human-written application review from Kaggle. Subsequently, we identify features of the fake review using natural language processing and statistical analysis. Next, we generate fake review detection models using five types of machine-learning models trained using identified features. In terms of the performances of the fake review detection models, we achieved average F1-scores of 0.738, 0.723, and 0.730 for the fake review, real review, and overall classifications, respectively.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• Clinical and Experimental Pediatrics
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• v.64 no.12
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• pp.642-651
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• 2021

Background: Infantile colic (IC) is excessive crying in otherwise healthy children. Despite vast research efforts, its etiology remains unknown. Purpose: Most treatments for IC carry various side effects. The collection of evidence may inform researchers of new strategies for the management and treatment of IC as well as new clues for understanding its pathogenesis. This review and meta-analysis aimed to evaluate the efficacy and possible mechanisms of probiotics for mananaging IC. Methods: Ten papers met the study inclusion and exclusion criteria, and the meta-analysis was conducted using Review Manager (RevMan) software and a random-effects model. Results: This meta-analysis revealed that probiotics are effective for treating infantile colic, while the review showed that this efficacy may be due to their anti-inflammatory effects. Conclusion: Probiotics may be an important treatment option for managing infantile colic due to their anti-inflammatory properties.

• The Korean Journal of Archival Studies
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• no.61
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• pp.157-190
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• 2019

The Records Appraisal Review contains the processes and results of appraisal works for records. This study is an analysis of the 2018 Records Appraisal Review of 22 Local Education Offices where records managers were deployed. To ensure reliability of analyzing results, the analysis data preparation criteria were applied to maintain consistency in the analysis data preparation. Also, statistics by appraisal process and retention period were cross-checked. Through the analysis of the Records Appraisal Review identifies the actual condition for records appraisal. Based on this, the improvement measures that are applicable in practice were proposed in terms of records management of department, appraisal works of records manager and operation of Records Appraisal Review Commission.

• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.209-217
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• 2016

As smartphone usage increases, the number of consumers who refer to review data of e-commercial products using web sites and SNS is also explosively multiplying. However, reading review data using traditional websites and SNS is time consuming. Also, it is impossible for consumers to read all the reviews. Therefore, a system that collects review data of products and conducts sentiment word analysis of the review is required to provide useful information. The majority of systems that provide such information inadequately reflect the properties of the product. In this study, we described a system that provides analysis and evaluation of e-commerce products through review sentiment words as reflected properties of the product. Furthermore, the system enables consumers to access processed information about reviews quickly and in visual format.

• Information Systems Review
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• v.23 no.1
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• pp.45-68
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• 2021

As the influence of online reviews on consumer decision-making increases, concerns about review manipulation are also increasing. Fake reviews or review manipulations are emerging as an important problem by posting untrue reviews in order to increase sales volume, causing the consumer's reverse choice, and acting at a high cost to the society as a whole. Most of the related prior studies have focused on predicting review manipulation through data mining methods, and research from a consumer perspective is insufficient. However, since the possibility of manipulation of reviews perceived by consumers can affect the usefulness of reviews, it can provide important implications for online word-of-mouth management regardless of whether it is false or not. Therefore, in this study, we analyzed whether there is a difference between the review evaluated by the consumer as being manipulated and the general review, and verified whether the manipulated review negatively affects the review usefulness. For empirical analysis, 34,711 online book reviews on the LibraryThing website were analyzed using multilevel logistic regression analysis and Poisson regression analysis. As a result of the analysis, it was found that there were differences in product level, reviewer level, and review level factors between reviews that consumers perceived as being manipulated and reviews that were not. In addition, manipulated reviews have been shown to negatively affect review usefulness.