• Journal of Information Technology Services
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• v.22 no.3
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• pp.141-165
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• 2023

With the advent of the fourth industrial revolution, technologies such as the Internet of Things, artificial intelligence and cloud computing are developing rapidly, and smart homes enabled by these technologies are rapidly gaining popularity. To gain a competitive advantage in the global market, companies must understand the differences in consumer needs in different countries and cultures and develop corresponding business strategies. Therefore, this study conducts a comparative analysis of consumer reviews of smart homes in South Korea and China. This study collected online reviews of SmartThings, ThinQ, Msmarthom, and MiHome, the four most commonly used smart home apps in Korea and China. The collected review data is divided into satisfied reviews and dissatisfied reviews according to the ratings, and topics are extracted for each review dataset using LDA topic modeling. Next, the extracted topics are classified according to five evaluation factors of Perceived Usefulness, Reachability, Interoperability,Trustness, and Product Brand proposed by previous studies. Then, by comparing the importance of each evaluation factor in the two datasets of satisfaction and dissatisfaction, we find out the factors that affect consumer satisfaction and dissatisfaction, and compare the differences between users in Korea and China. We found Trustness and Reachability are very important factors. Finally, through language network analysis, the relationship between dissatisfied factors is analyzed from a more microscopic level, and improvement plans are proposed to the companies according to the analysis results.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.2
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• pp.331-352
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• 2014

In this study, the picture book reviews shown in Korean periodicals were analyzed to provide the developmental foundation for picture book reviews and expand the professional area of librarians as the producers of book review. Through the literature research and the Delphi Technique the desirable standard of book reviews was suggested, the contents in domestic book review mediums were analyzed for the rightness of describing based on the standard of the book review and henceforward the way of development was suggested. The result from analysis of book review showed that the outstanding book reviews should be more produced based on the desirable standard of book review and the evaluative statements should be developed more than the descriptive statements. Depending on the mediums the contents of the book review were different by the book reviewers' occupations, so the book reviewers should develop their professional knowledges in literature and art. Because the longer book review made the outstanding book review with the rightness of the desirable standard of book review, the certain quantity of book review should be needed and the role of librarians as the book reviewers should be further extended.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.87-94
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• 2020

Social reviews such as SNS feeds and blog articles have been widely used to extract keywords reflecting opinions and complaints from users' perspective, and often include proper nouns or new words reflecting recent trends. In general, these words are not included in a dictionary, so conventional morphological analyzers may not detect and extract those words from the reviews properly. In addition, due to their high processing time, it is inadequate to provide analysis results in a timely manner. This paper presents a method for efficient keyword extraction from social reviews based on the notion of cohesion scoring. Cohesion scores can be calculated based on word frequencies, so keyword extraction can be performed without a dictionary when using it. On the other hand, their accuracy can be degraded when input data with poor spacing is given. Regarding this, an algorithm is presented which improves the existing cohesion scoring mechanism using the structure of a word tree. Our experiment results show that it took only 0.008 seconds to extract keywords from 1,000 reviews in the proposed method while resulting in 15.5% error ratio which is better than the existing morphological analyzers.

• Health Policy and Management
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• v.17 no.4
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• pp.82-98
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• 2007

This study aimed to analyze the factors that influence the job satisfaction of nurses involved in medical insurance reviews. The study involved a self-administered questionnaire survey which was conducted with to 297 nurses who were in charge of medical insurance reviews between April 10 and April 28, 2000. The average job satisfaction of the subjects was 3.04. The sub-items of job satisfaction were noted to be high for 'professional status'(3.79) and low for wage (2.46). The job satisfaction of subjects showed statistically significant differences with regard to education, career, and volume of service(p<0.05). The average job stress of subjects was 2.57. The sub-items of job stress included problems pertaining to human relationships problem(2.84), conflicts with doctors at work (2.79), and the burden of excessive workloads(2.79), in that order. Multiple regression analysis demonstrated that job satisfaction was significantly low when the job stress was higher. It also showed that the job satisfaction was significantly high as there was more frequency of judgements and higher education. These results suggest that the job stress of nurses involved in medical insurance reviews has a profound impact on their job satisfaction. Therefore, the efforts should be made to reduce their job stress. It may also be beneficial to reinforce the training with the doctors and nurses in order to improve their communication skills. Disseminating more information about insurance standards may also be considered.

• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.497-502
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• 2009

In this paper, we introduce an automatic product feature extracting system that improves the efficiency of product review analysis. Our system consists of 2 parts: a review collection and correction part and a product feature extraction part. The former part collects reviews from internet shopping malls and revises spoken style or ungrammatical sentences. In the latter part, product features that mean items that can be used as evaluation criteria like 'size' and 'style' for a skirt are automatically extracted by utilizing term statistics in reviews and web documents on the Internet. We choose nouns in reviews as candidates for product features, and calculate degree of association between candidate nouns and products by combining inner association degree and outer association degree. Inner association degree is calculated from noun frequency in reviews and outer association degree is calculated from co-occurrence frequency of a candidate noun and a product name in web documents. In evaluation results, our extraction method showed an average recall of 90%, which is better than the results of previous approaches.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.515-528
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• 2023

With the growth of the e-commerce market, consumers increasingly rely on user reviews to make purchasing decisions. Consequently, researchers are actively conducting studies to effectively analyze these reviews. Among the various methods of sentiment analysis, the aspect-based sentiment analysis approach, which examines user reviews from multiple angles rather than solely relying on simple positive or negative sentiments, is gaining widespread attention. Among the various methodologies for aspect-based sentiment analysis, there is an analysis method using a transformer-based model, which is the latest natural language processing technology. In this paper, we conduct an aspect-based sentiment analysis on multilingual user reviews using two real datasets from the latest natural language processing technology model. Specifically, we use restaurant data from the SemEval 2016 public dataset and multilingual user review data from the cosmetic domain. We compare the performance of transformer-based models for aspect-based sentiment analysis and apply various methodologies to improve their performance. Models using multilingual data are expected to be highly useful in that they can analyze multiple languages in one model without building separate models for each language.

• Journal of Distribution Science
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• v.11 no.4
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• pp.13-21
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• 2013

Purpose - The purpose of this research is to find out the relationship between cue utilization and perceived website quality and purchase intention for an online store. To achieve this, we suggest a conceptual model that examines the relationship among product introductions, online communications, online reviews, perceived quality, and online purchase intention. Research design, data, and methodology - This research utilizes SPSS 19.0 and AMOS17.0 to analyze the data. We used factor analysis to shape the structure of the original data and saved the information with multiple dimensions. We then deployed the AMOS software to analyze the model. We performed both factor analysis and structural equation analysis. Results - The findings of this study show that graphic and word descriptions, online chatting, and online reviews have a positive influence on perceived quality. Furthermore, perceived quality has a positive influence on purchase intention. Conclusions - First, detailed product information should be added to influence quality perception. Second, consumers expect a certain level of service while shopping. Simultaneously, online products reviews from consumers deserve attention as they can impact consumer purchase intention.

• Journal of Information Technology Applications and Management
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• v.31 no.3
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• pp.93-104
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• 2024

This study conducts an in-depth analysis of user reviews from three leading metaverse platforms - Minecraft, Roblox, and Zepeto - using advanced topic modeling techniques to uncover key factors for business success. By examining a substantial dataset of user feedback, we identified and categorized the main themes and concerns expressed by users. Our analysis revealed that common issues across all platforms include technical functionality problems, user engagement and interest, payment concerns, and connection difficulties. Specifically, Minecraft users highlighted the importance of adventure and creativity, Roblox users expressed significant concerns about security and fraud, and Zepeto users focused heavily on the fairness of the in-game economy. The findings suggest that for metaverse platforms to achieve sustained success, they must prioritize the resolution of technical issues, enhance features that foster user engagement, ensure reliable connectivity, and address platform-specific concerns such as security for Roblox and payment fairness for Zepeto. These insights provide valuable guidance for developers and business strategists, emphasizing the need for robust technical infrastructure, engaging and diverse content, seamless user access, and transparent and fair economic systems. By addressing these key areas, metaverse platforms can improve user satisfaction, build a loyal user base, and secure long-term success in an increasingly competitive market.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.87-97
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• 2019

Reaction of people is importantly considered about specific case as a social network service grows. In the previous research on analysis of social network service, they predicted tendency of interesting topic by giving scores to sentences written by user. Based on previous study we proceeded research of sentiment analysis for social network service's sentences, which predict the result as positive or negative for movie reviews. In this study, we used movie review to get high accuracy. We classify the movie review into positive or negative based on the score for learning. Also, we performed embedding and morpheme analysis on movie review. We could predict learning result as positive or negative with a number 0 and 1 by applying the model based on learning result to social network service. Experimental result show accuracy of about 80% in predicting sentence as positive or negative.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.392-403
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• 2008

User reviews are valuable information that can be used for various purposes. In particular, the product reviews on online shopping sites are important information which can directly affect the purchasing decision of the customers. In this paper, we present our design and implementation of a system for summarizing the customer's opinion and the features of each product by analyzing reviews on a commercial shopping site. During the analysis process, several natural language processing(NLP) techniques and the semantic dictionary were used. The semantic dictionary contains vocabularies that are used to express product features and customer's opinions. And it was constructed in semi-automatic way with the help of the tool we implemented. Furthermore, we discuss how to handle the vocabularies that have different meanings according to the context. We analyzed 1796 reviews about 20 products of 2 categories collected from an actual shopping site and implemented a novel ranking system. We obtained 88.94% for precision and 47.92% for recall on extracting opinion expression, which means our system can be applicable for real use.