• 품질경영학회지
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• 제51권4호
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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.

• 패션비즈니스
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• 제26권3호
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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.

• 예술인문사회 융합 멀티미디어 논문지
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• 제8권3호
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• pp.531-541
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• 2018

본 연구는 소비자들의 농협보험에 대한 이미지 이미지를 분석하기 위해 텍스트 마이닝과 텍스트 네트워크 분석을 실시하였다. 최근 소셜미디어의 발달로 많은 텍스트가 생산 및 재생산되고 있으며, 텍스트는 기업에게 중요한 정보들을 제공한다. 이러한 정보의 의미를 도출하기 위해, 텍스트 마이닝과 텍스트 네트워크 분석을 많은 연구에서 실시하고 있다. 텍스트 분석결과, 농협보험의 긍정적 이미지는 주로 안전과 안정으로 나타났다. 농협보험의 부정적 이미지로는 우려와 불안으로 나타났다. 텍스트 네트워크 분석을 통해 도출한 농협보험의 이미지는 안전과 우려를 중심으로 형성되었다. 텍스트 네트워크 분석을 통해 도출된 결과를 인터뷰를 통해 확인하였다. 인터뷰 결과, 농협은 자산규모 등을 통해 안정적인 재무와 보험금 지급은 안전함이 긍정적 이미지의 주요한 요인이었다. 부정적 이미지로는 최근의 정보유출 사태로 인해 소비자들의 개인정보유출에 대한 우려가 큰 것으로 나타났다. 본 연구에서 분석을 통해 타 상품의 이미지 분석도 사용가능할 것이다.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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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.

• 한국정보시스템학회지:정보시스템연구
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• 제29권4호
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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.

• 대한산업공학회지
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• 제38권1호
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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.

• 한국공간구조학회논문집
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• 제21권3호
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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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• 제30권2호
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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.

• 디지털융복합연구
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• 제18권7호
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• pp.93-99
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• 2020

본 연구에서는 비정형적인 대용량의 텍스트 자료로부터 유의미한 정보를 추출하는 빅데이터 분석방법 중 텍스트 마이닝을 이용하여 시행 중인 정책과 제도에 대한 시민의견을 모니터링 할 수 있는지 확인하였다. '경기지역화폐'와 관련된 5,108건의 신문기사와 748건의 온라인 카페글을 수집하여 빈도분석, TF-IDF분석, 연관분석, 워드트리 시각화 분석을 수행하였다. 그 결과로 기사에서는 지역화폐의 도입 목적, 제공되는 혜택, 사용방법에 관련된 내용이 많았고 카페글에서는 지역화폐의 실사용과 관련된 내용 위주로 작성이 되어있음을 확인하였다. 또한 지역화폐 활성화를 위해서 뉴스는 정보전달자로서 지역화폐의 홍보에 관여하고 있었고 카페글은 지역화폐 사용자인 시민들의 의견으로 이루어져 사용과 관련된 실제적인 정보 교환의 장으로 기능하고 있었다. 지역화폐뿐만 아니라 다양한 정책과 제도에 관해서도 SNS와 텍스트 마이닝을 통해 시민들의 의견을 수렴하여 효과적으로 활성화시킬 수 있을 것으로 보인다.

• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.119-125
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• 2019

The purpose of this study is to find out the relevant factors of the choice tendency of tourism users to Intelligent Tourism platform through big data analysis, which will help enterprises to make accurate positioning and improvement according to user information feedback in the tourism market in the future, so as to gain the favor of users' choice and achieve long-term market competitiveness. This study takes the Intelligent Tourism platform as the independent variable and the user choice tendency as the dependent variable, and explores the related factors between the Intelligent Tourism platform and the user choice tendency. This study make use of text mining and R language text analysis, and uses SPSS and AMOS statistical analysis tools to carry out empirical analysis. According to the analysis results, the conclusions are as follows: service quality has a significant positive correlation with user choice tendency; service quality has a significant positive correlation with tourism trust; Tourism Trust has a significant positive correlation with user choice tendency; service quality has a significant positive correlation with user experience; user experience has a significant positive correlation with user choice tendency Positive correlation effect.