• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.701-713
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• 2023

Purpose: The primary purpose of this study is to integrate text mining and Quality Function Deployment (QFD) to automatically extract valuable information from customer reviews, thereby establishing a QFD frame- work to confirm genuine customer needs for New Product Development (NPD). Methods: Our approach combines text mining and QFD through topic modeling and sentiment analysis on a large data set of 56,873 customer reviews from Zappos.com, spanning five running shoe brands. This process objectively identifies customer requirements, establishes priorities, and assesses competitive strengths. Results: Through the analysis of customer reviews, the study successfully extracts customer requirements and translates customer experience insights and emotions into quantifiable indicators of competitiveness. Conclusion: The findings obtained from this research offer essential design guidance for new product develop- ment endeavors. Importantly, the significance of these results extends beyond the running shoe industry, presenting broad and promising applications across diverse sectors.

• Journal of Fashion Business
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• v.26 no.3
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• pp.138-154
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• 2022

The purpose of this study is to identify the characteristics of domestic American casual fashion trends using big data text mining analysis. 108,524 posts and 2,038,999 extracted keywords from Naver and Daum related to American casual fashion in the past 5 years were collected and refined by the Textom program, and frequency analysis, word cloud, N-gram, centrality analysis, and CONCOR analysis were performed. The frequency analysis, 'vintage', 'style', 'daily look', 'coordination', 'workwear', 'men's wear' appeared as the main keywords. The main nationality of the representative brands was Japanese, followed by American, Korean, and others. As a result of the CONCOR analysis, four clusters were derived: "general American casual trend", "vintage taste", "direct sales mania", and "American styling". This study results showed that Japanese American casual clothes are influenced by American casual clothes, and American casual fashion in Korea, which has been reinterpreted, is completed with various coordination and creative styles such as workwear, street, military, classic, etc., focusing on items and brands. Looks were worn and shared on social networks, and the existence of an active consumer group and market potential to obtain genuine products, ranging from second-hand transactions for limited edition vintages to individual transactions were also confirmed. The significance of this study is that it presented the characteristics of American casual fashion trends academically based on online text data that the public actually uses because it has been spread by the public.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.531-541
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• 2018

This study researched text mining and text network analysis to analyze the images of Nonghyup Insurance for consumers. With the recent development of social media, many texts are being produced and reproduced, and texts of social media provide important information to companies. Text mining and text network analysis are used in many studies to identify image of company and product. As a result of the text analysis, the positive image of the Nonghyup Insurance is safety and stability. Negative images of the Nonghyup Insurance is concern and anxiety. As a result of the textual network analysis, Centered mage of Nonghyup Insurance is safety and concern. This paper allows researchers to extract several lessons learned that are important for the text mining and text network analysis.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.101-106
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• 2005

Recently, as the size of genetic knowledge grows faster, automated analysis and systemization into high-throughput database has become hot issue. One essential task is to recognize and identify genomic entities and discover their relations. However, ambiguity of name entities is a serious problem because of their multiplicity of meanings and types. So far, many effective techniques have been proposed to analyze documents. Yet, accuracy is high when the data fits the model well. The purpose of this paper is to design and implement a document classification system for identifying entity problems using text/data mining combination, supplemented by rich data mining algorithms to enhance its performance. we propose RTP ost system of different style from any traditional method, which takes fault tolerant system approach and data mining strategy. This feedback cycle can enhance the performance of the text mining in terms of accuracy. We experimented our system for classifying RB-related documents on PubMed abstracts to verify the feasibility.

• The Journal of Information Systems
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• v.29 no.4
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• pp.137-151
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• 2020

Purpose Domestic asset management fintech companies are expected to grow by leaps and bounds along with the implementation of the "Data bills." Contrary to the market fever, however, academic research is insufficient. Therefore, we want to analyze user reviews of asset management fintech companies that are expected to grow significantly in the future to derive strengths and complementary points of services that have been provided, and analyze key elements of asset management fintech companies. Design/methodology/approach To analyze large amounts of review text data, this study applied text mining techniques. Bank Salad and Toss, domestic asset management application services, were selected for the study. To get the data, app reviews were crawled in the online app store and preprocessed using natural language processing techniques. Topic Modeling and Aspect-Sentiment Analysis were used as analysis methods. Findings According to the analysis results, this study was able to derive the elements that asset management fintech companies should have. As a result of Topic Modeling, 7 topics were derived from Bank Salad and Toss respectively. As a result, topics related to function and usage and topics on stability and marketing were extracted. Sentiment Analysis showed that users responded positively to function-related topics, but negatively to usage-related topics and stability topics. Through this, we were able to extract the key elements needed for asset management fintech companies.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.221-226
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• 2024

This study used text mining, a big data analysis technique, to explore XR trends in South Korea. For this research, I utilized a big data platform called BigKinds. I collected data focusing on the keyword 'XR', spanning approximately 14 years from 2010 to 2024. The gathered data underwent a cleansing process and was analyzed in three ways: keyword trend analysis, relational analysis, and word cloud. The analysis identified the emergence and most active discussion periods of XR, with XR devices and manufacturers emerging as key keywords.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.1
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• pp.67-73
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• 2012

Identification of meaningful patterns and trends in large volumes of text data is an important task in various research areas. In the present study we crawled the keywords from the abstracts in IIE Transactions, one of the representative journals in the field of Industrial Engineering from 1969 to 2011. We applied low-dimensional embedding method, clustering analysis, association rule, and social network analysis to find meaningful associative patterns of key words frequently appeared in the paper.

• Journal of Korean Association for Spatial Structures
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• v.21 no.3
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• pp.85-92
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• 2021

The purpose of this paper is to understand the key factors for efficient maintenance of rapidly aging facilities. Therefore, the safety inspection/diagnosis reports accumulated in the unstructured data were collected and preprocessed. Then, the analysis was performed using a text mining analysis method. The derived vulnerabilities of tunnel facilities can be used as elements of inspections that take into account the characteristics of individual facilities during regular inspections and daily inspections in the short term. In addition, if detailed specification information and other inspection results(safety, durability, and ease of use) are used for analysis, it provides a stepping stone for supporting preemptive maintenance decision-making in the long term.

• Journal of Information Technology Applications and Management
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• v.30 no.2
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• pp.19-30
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• 2023

This study aims to analyze service improvement and success factors of electric scooter sharing service companies by using text mining after collecting reviews of shared electric scooter service applications among various models of sharing economy. In this study, the factors of satisfaction and dissatisfaction of service users were identified using the term frequency inverse document frequency (TF-IDF) technique, and topics for each keyword were extracted using the Latent Dirichlet Allocation (LDA) Topic Modeling technique. According to the analysis results, the main topics were entertainment, safety, service area, application complaints, use complaints, convenience, and mobility. Using the analysis results of this study, employees and researchers of electric scooter sharing service companies will be able to contribute to the improvement and success of related services.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.93-99
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• 2020

Text mining is one of the big data analysis methods that extracts meaningful information from atypical large-scale text data. In this study, text mining was used to monitor citizens' opinions on the policies and systems being implemented. We collected 5,108 newspaper articles and 748 online cafe posts related to 'Gyeonggi Lacal Currency' and performed frequency analysis, TF-IDF analysis, association analysis, and word tree visualization analysis. As a result, many articles related to the purpose of introducing local currency, the benefits provided, and the method of use. However, the contents related to the actual use of local currency were written in the online cafe posts. In order to revitalize local currency, the news was involved in the promotion of local currency as an informant. Online cafe posts consisted of the opinions of citizens who are local currency users. SNS and text mining are expected to effectively activate various policies as well as local currency.