• Journal of Distribution Science
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• v.22 no.3
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• pp.113-124
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• 2024

Purpose: This study is a quantitative study and analyzes the effect of evaluating the extreme and usefulness of product reviews on sales performance by using text mining techniques based on product review big data. We investigate whether the perceived helpfulness of product reviews serves as a mediating factor in the impact of product review extremity on sales performance. Research design, data and methodology: The analysis emphasizes customer interaction factors associated with both product review helpfulness and sales performance. Out of the 8.26 million Amazon product reviews in the book category collected by He & McAuley (2016), text mining using natural language processing methodology was performed on 300,000 product reviews, and the hypothesis was verified through hierarchical regression analysis. Results: The extremity of product reviews exhibited a negative impact on the evaluation of helpfulness. And the helpfulness played a mediating role between the extremity of product reviews and sales performance. Conclusion: Increased inclusion of extreme content in the product review's text correlates with a diminished evaluation of helpfulness. The evaluation of helpfulness exerts a negative mediating effect on sales performance. This study offers empirical insights for digital market distributors and sellers, contributing to the research field related to product reviews based on review ratings.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.581-590
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• 2024

This study aims to extract key topics through text mining of incident records (incident history, post-incident measures, preventive measures) from construction safety accident case data available on the public data portal. It also seeks to provide fundamental insights contributing to the establishment of manuals for disaster prevention by identifying correlations between these topics. After pre-processing the input data, we used the LDA-based topic modeling technique to derive the main topics. Consequently, we obtained five topics related to incident history, and four topics each related to post-incident measures and preventive measures. Although no dominant patterns emerged from the topic pattern analysis, the study holds significance as it provides quantitative information on the follow-up actions related to the incident history, thereby suggesting practical implications for the establishment of a preventive decision-making system through the linkage between accident history and subsequent measures for reccurrence prevention.

• Journal of Navigation and Port Research
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• v.45 no.3
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• pp.95-101
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• 2021

Marine Incidents based on Heinrich's law are very important in preventing accidents. However, marine Incident data are mainly qualitative and are used to prevent similar accidents through case sharing rather than statistical analysis, which can be confirmed in the marine Incident-related data posted in the Korea Maritime Safety Tribunal. Therefore, this study derived quantitative results by analyzing the causes of marine incidents during berthing using various methods of statistical analysis. To this end, data involving marine incidents from various shipping companies were collected and reclassified for easy analysis. The main keywords were derived via primary analysis using text mining. Only meaningful words were selected via verification by an expert group, and time series and cluster analysis were performed to predict marine incidents that may occur during berthing. Although the role of an expert group was still required during the analysis, it was confirmed that quantitative analysis of marine incidents was feasible, and iused to provide cause and accident prevention information.

• Journal of Korean Medical classics
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• v.31 no.2
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• pp.71-82
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• 2018

Objectives : The purpose of this study is to compare the similarities among Donguibogam(DO), Uihagibmun(UI), and Gyeongagjeonseo(GY) in order to examine whether the medical thoughts embedded in the texts can be compared in a quantitative way. Methods : Under an empirical assumption that medical thoughts can be reduced to the frequency of major key words within the text, we selected the fourteen words of the four categories that are commonly used to describe physiology and pathology in Korean medicine as key words. And the frequency of these key words was measured and compared with each other in the three important medical texts in Korea. Results : As a result of quantitative analysis based on ${\chi}^2$ statistic, the key words in the books were distributed most heterogeneously in DO and distributed most homogeneously in UI. In comparison of the similarity analyzed by the same method, DO and UI were significantly more similar than those of DO and UI. The results of the word frequency pattern and the similarities of the book contents(CBDF) show that DO is influenced by UI, and the differences between standardized residuals and homogeneity tells us that internal context of both books are constructed differently. Conclusions : These results support the results of traditional research by experts. With the above, we were able to confirm that medical thoughts can be reduced to the frequency of major key words within the text, and compared through the frequency of such key words.

• The Journal of Information Systems
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• v.32 no.3
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• pp.19-39
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• 2023

Purpose This study aims to identify user experience factors that can enhance both metaverse utilization and satisfaction based on the honeycomb model. For this we presented two research questions: first, what are the experience factors of metaverse users? Second, do metaverse user experience factors impact satisfaction? Design/methodology/approach To address these questions, a mixed-methodology approach is employed, including text mining techniques to analyze online reviews and quantitative econometric analysis to reveal the relationship between user experience factors and satisfaction. A total of 69,880 reviews and ratings data were collected. Findings The analysis revealed eight metaverse user experience factors: entertainment, operability, virtual reality, immersion, economic activity, visual performance, avatar, and sociality, all of which were found to have a positive impact on user satisfaction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.46-54
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• 2017

'Consulting', which is the main research topic of the knowledge service industry, is a field of study that is essential for the growth and development of companies and proliferation to specialized fields. However, it is difficult to grasp the current status of international research related to consulting, mainly on which topics are being studied, and what are the latest research topics. The purpose of this study is to analyze the research trends of academic research related to 'consulting' by applying quantitative analysis such as topic modeling and statistic analysis. In this study, we collected statistical data related to consulting in the Scopus DB of Elsevier, which is a representative academic database, and conducted a quantitative analysis on 15,888 documents. We scientifically analyzed the research trends related to consulting based on the bibliographic data of academic research published all over the world. Specifically, the trends of the number of articles published in the major countries including Korea, the author key word trend, and the research topic trend were compared by country and year. This study is significant in that it presents the result of quantitative analysis based on bibliographic data in the academic DB in order to scientifically analyze the trend of academic research related to consulting. Especially, it is meaningful that the traditional frequency-based quantitative bibliographic analysis method and the text mining (topic modeling) technique are used together and analyzed. The results of this study can be used as a tool to guide the direction of research in consulting field. It is expected that it will help to predict the promising field, changes and trends of consulting industry related research through the trend analysis.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.655-667
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• 2014

This paper has identified five major categories of the T2D patients' concerns based on an online forum where the patients voluntarily verbalized their naturally occurring emotional reactions and concerns related to T2D. We have emphasized the fact that the lexical and phrasal analysis brought to the forefront the prevailing negative reactions and desires for clear information, professional advice, and emotional support. This study used lexical and phrasal analysis based on text-mining tools to estimate the potential of using a large sample of patient conversation of a specific disease posted on the internet for clinical features and patients' emotions. As a result, the study showed that quantitative analysis based on text-mining is a viable method of generalizing the psychological concerns and features of T2D patients.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.37-48
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• 2020

This study aimed at understanding trends of living lab studies and deriving implications for directions of the studies by utilizing text mining. The study included network analysis and topic modelling based on keywords and abstracts from total 166 thesis published between 2011 and November 2019. Centrality analysis showed that living lab studies had been conducted focusing on keywords like innovation, society, technology, development, user and so on. From the topic modelling, 5 topics such as "regional innovation and user support", "social policy program of government", "smart city platform building", "technology innovation model of company" and "participation in system transformation" were extracted. Since the foundation of KNoLL in 2017, the diversification of living lab study subjects has been made. Quantitative analysis using text mining provides useful results for development of living lab studies.

• Journal of Wellbeing Management and Applied Psychology
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• v.5 no.1
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• pp.37-46
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• 2022

Purpose: This study examines the history of the evolution of MS analysis and intends to consider the future direction of technological development through the difference from the latest technology, SIFT-MS. Research design, data and methodology: A method of analysis will be described in detail at the below by SIFT-MS (Selected Ion Flow Mass Spectrometry), which is a technology developed by a company called SIFT Technologies. Results: The initial concept of mass spectrometry was begun in the late 1890s, and it continues to evolve even after the 21st century through the ripening stage of the 20th century. The development process of mass spectrometry by year has been described in detail in the Main text. Conclusions: Mass spectrometry, qualitative and quantitative analysis of substances plays a very important role in the research and medical fields. The development of these analytical methods is expected to continue in the future, and faster and more accurate qualitative analysis and mass spectrometry will be developed than the level currently reached. In addition, it is expected that hardware and software will be configured so that non-analysis experts can handle it easily, and it will be used as a technology that is more closely related to our lives.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.291-298
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• 2012

Objective: The aim of this study is to predict Korean text entry on touchscreens using ACT-R cognitive architecture. Background: Touchscreen application in devices such as satellite navigation devices, PDAs, mobile phones, etc. has been increasing, and the market size is expanding. Accordingly, there is an increasing interest to develop and evaluate the interface to enhance the user experience and increase satisfaction in the touchscreen environment. Method: In this study, Korean text entry performance in the touchscreen environment was analyzed using ACT-R. The ACT-R model considering the characteristics of the Korean language which is composed of vowels and consonants was established. Further, this study analyzed if the prediction of Korean text entry is possible through the ACT-R cognitive model. Results: In the analysis results, no significant difference on performance time between model prediction and empirical data was found. Conclusion: The proposed model can predict the accurate physical movement time as well as cognitive processing time. Application: This study is useful in conducting model-based evaluation on the text entry interface of the touchscreen and enabled quantitative and effective evaluation on the diverse types of Korean text input interfaces through the cognitive models.