• Tunnel and Underground Space
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• v.25 no.4
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• pp.303-319
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• 2015

Text mining is one of the branches of data mining and is used to find any meaningful information from the large amount of text. In this study, we analyzed titles and keywords of two SCI journals on rock engineering by using text mining to find major research area, trend and associations of research fields. Visualization of the results was also included for the intuitive understanding of the results. Two journals showed similar research fields but different patterns in the associations among research fields. IJRMMS showed simple network, that is one big group based on the keyword 'rock' with a few small groups. On the other hand, RMRE showed a complex network among various medium groups. Trend analysis by clustering and linear regression of keyword - year frequency matrix provided that most of the keywords increased in number as time goes by except a few descending keywords.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.272-284
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• 2009

In this paper, we proposed the methodology for analyzing online customer reviews by using text mining technologies. We introduced marketing segmentation into the methodology because it would be efficient and effective to analyze the online customers by grouping them into similar online customers that might include similar opinions and experiences of the customers. That is, the methodology uses categorization and information extraction functions among text mining technologies, matched up with the concept of market segmentation. In particular, the methodology also uses cross-tabulations analysis function which is a kind of traditional statistics analysis functions to derive rigorous results of the analysis. In order to confirm the validity of the methodology, we actually analyzed online customer reviews related with tourism by using the methodology.

• 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.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.217-234
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• 2018

In this study, big data analysis was conducted for domestic and international sports goods brands. Text Mining, TF-IDF, Opinion Mining, interestity graph were conducted through the social matrix program Textom and the fashion data analysis platform MISP. In order to examine the recent recognition of sports brands, the period of study is limited to 1 year from January 1, 2017 to December 31, 2017. As a result of analysis, first, we could confirm the products representing each brand. Second, I could confirm the marketing that represents each brand. Third, the common words extracted from each brand were identified. Fourth, the emotions of positive and negative of each brand were confirmed.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.355-360
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• 2014

Forecasting of emerging technology plays important roles in business strategy and R&D investment. There are various ways for technology forecasting including patent analysis. Qualitative analysis methods through experts' evaluations and opinions have been mainly used for technology forecasting using patents. However qualitative methods do not assure objectivity of analysis results and requires high cost and long time. To make up for the weaknesses, we are able to analyze patent data quantitatively and statistically by using text mining technique. In this paper, we suggest a new method of technology forecasting using text mining and ARIMA analysis.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.2
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• pp.285-308
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• 2010

This study analyzes the intellectual structure in domestic research of the Housing field, by utilizing text mining technique. Unlike the existing research that mainly uses text clustering in statistical analyses to identify subject specialties, core authors, and relationships between research areas, this study applied author profiling and factor analysis. To supplement the analysis of intellectual structure generated by text mining, and to perform evaluation on intellectual structure itself, two professionals in the housing field were interviewed. The intellectual structure, generated through text mining, was evaluated and showed its division of valid research areas that is slightly different from the traditional intellectual structure in the housing field.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.13-19
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• 2024

Purpose: This study provides prevention methods by type of construction disaster using text mining techniques. Method: Based on the database that analyzed the cases of critical disasters in the domestic construction sector, preventive measures and causes are analyzed by text mining techniques, and the contents of the analysis are visually shown. Result: This visual data represents the measures for preventing critical disasters of each process according to the importance. Conclusion: It is believed that the results will be helpful in identifying factors to be considered in preparing preventive measures for serious accidents in construction.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.364-369
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• 2010

본 논문에서는 뉴스 텍스트 마이닝을 수행하여 2005년 1월부터 2008년 12월까지 4년 간의 뉴스 데이터에 대해 주가에 호재인지 악재인지 여부에 대해 학습을 하고, 이를 근거로 신규 발행된 뉴스가 주가 상승 또는 하락에 영향을 미치는지를 예측하는 알고리즘을 제안한다. 뉴스 텍스트 마이닝을 위해 변형된 Bag of Words 모델과 Naive Bayesian 분류기법을 사용하였으며, 특히 주가 예측에 있어서 뉴스 마이닝에만 의존하던 기존의 관련 연구와는 달리 예측의 정확성을 높이기 위해 주가의 시계열 데이터 분석기법인 RSI를 추가로 작용하였다. 2009년 11월부터 2010년 2월까지 4개월간 42,355건의 뉴스 데이터에 대해 실험한 결과, 기존 연구 대비 의미 있는 결과인 55.01%의 예측성공률을 얻었다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.316-318
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• 2000

데이터마이닝 기능은 문서의 구조화되지 않은 텍스트보다는 테이블과 일반적인 DB에 있는 구조화된 자료에 초점이 맞춰져 있다. 정보화의 과정속에서 많은 기업이나 조직들은 과거의 시스템을 DB로 구축하여 어느 정도 형태를 갖추게 되었지만, E-business, E-commerce가 활발해지면서 보유하고 있는 DB기반이 아닌 무작위의 새로운 데이터가 사용자들에 의해 생성되기도 한다. 본 논문에서는 이러한 텍스트 문서에 숨어있는 정보들을 발견하기 위한 텍스트마이닝 과정을 시나리오로 설정하고, 문서와 문서집합에 대해 분석도구를 적용하는 어플리케이션을 구현해 보았다. 대규모의 문서집합에 분석도구를 이용함으로써 빠른 문서처리가 가능하고 이는 사용자가 많은 양의 문서들을 다룰 때의 시간비용을 최소화시킬 수 있는 방법이 될 수 있다. 또한 마이닝과정을 통해 발견한 지식과 특징들을 기반으로 반구조화된 파일로 변환하여, 규칙발견, 데이터마이닝기법을 적용하여 의미있는 새로운 결론을 얻을 수 있을 것이다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.529-536
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• 2023

The secondhand market in Korea has been showing steady growth. However, the number of fraud cases and the amount of damages from fraudulent activities in secondhand transactions are also increasing. As of 2021, the size of the secondhand market reached 24 trillion won, but the total amount of fraud-related damages reached 360.6 billion won. In order to prevent fraud between individuals, secondhand trading platforms have implemented a safety payment system. However, new types of fraud methods exploiting the safety payment system have emerged, undermining the security of secondhand transaction safety payments. In this study, we aim to utilize text mining to examine the current state of the safety payment system in secondhand transactions and propose improvement measures by analyzing the system through text mining and network analysis.