• 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.

• Journal of the Korea Convergence Society
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• v.9 no.4
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• pp.15-22
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• 2018

The purpose of this study is to investigate the consumer perception of packaging using big data analysis. This study use text mining to extract meaningful words from text and semantic network analysis to analyze connectivity and propagation trends. Data were collected by dividing the 'packaging(Korean)' and 'packaging(English)'. This study visualized the word network structure of the two key words and classified them into four groups with similar meaning through CONCOR analysis. The group name was specified based on the words constituting the classified group. These groups are a major category of consumers' perception of packaging. Especially cosmetics and design have high frequency of words and high centrality. Therefore it can be expected that the packaging design is perceived as important in the cosmetics industry. This study predicts consumers' perception of packaging so it can be a basis for future research and industry development.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.17-27
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• 2021

The aim of this research was to identify the issue of the work permit system and consciousness of the people on the system, and to suggest some ideas on the government policies on it. To achieve the aim of research, this research used text mining based on social data. This research collected 1,453,272 texts from 6,217 units of online documents which contained 'work permit system' from January to December, 2020 using Textom, and did text-mining and social network analysis. This research extracted 100 key words frequently mentioned from the analyses of data top-level key word frequency, and degree centrality analysis, and constituted job problem, importance of policy process, competitiveness in the respect of industries, and improvement of living conditions of foreign workers as major key words. In addition, through semantic network analysis, this research figured out major awareness like 'employment policy', and various kinds of ambient awareness like 'international cooperation', 'workers' human rights', 'law', 'recruitment of foreigners', 'corporate competitiveness', 'immigrant culture' and 'foreign workforce management'. Finally, this research suggested some ideas worth considering in establishing government policies on the work permit system and doing related researches.

• Informatization Policy
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• v.30 no.2
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• pp.68-85
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• 2023

In this study, we collected data from live commerce streaming. Streamimg data were then categorized based on the degree of chatting activation, with the distribution of text responses generated by consumers analyzed. From a total of 2,282 streaming data on NAVER Shopping Live -which has the largest share in the domestic live commerce market- we selected 200 streaming data with the most active viewer responses and finally chose the streams that had steep increase or decrease in viewer responses. We synthesized variables from the existing literature on live commerce viewing intentions and participation motivations to create a table of variables for the purpose of the study. Then we applied them with events in the broadcast. Through this study, we identified which components of the broadcast stimulate the variables of consumer response found in previous studies, moreover, we empirically identified the motivations of consumers to participate in live commerce through data.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.277-284
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• 2020

Text mining is a representative method of big data analysis that extracts meaningful information from unstructured and large amounts of text data. Social media such as Twitter generates hundreds of thousands of data per second and acts as a one-person media that instantly and directly expresses public opinions and ideas. The traditional media are delivering informations, criticizing society, and forming public opinions. For this, we compare the responses of SNS with the responses of media on the issue of the termination of the Korea-Japan GSOMIA (General Security of Military Information Agreement), one of the domestic issues in the second half of 2019. Data collected from 201,728 tweets and 20,698 newspaper articles were analyzed by sentiment analysis, association keyword analysis, and cluster analysis. As a result, SNS tends to respond positively to this issue, and the media tends to react negatively. In association keyword analysis, SNS shows positive views on domestic issues such as "destruction, decision, we," while the media shows negative views on external issues such as "disappointment, regret, concern". SNS is faster and more powerful than media when studying or creating social trends and opinions, rather than the function of information delivery. This can complement the role of the media that reflects public perception.

• Journal of KIISE
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• v.42 no.4
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• pp.468-474
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• 2015

Corporations should make efforts to recognize the importance of projects, identify their failure factors, prevent risks in advance, and raise the success rates, because the corporations need to make quick responses to rapid external changes. There are some previous studies on success and failure factors of projects, however, most of them have limitations in terms of objectivity and quantitative analysis based on data gathering through surveys, statistical sampling and analysis. This study analyzes the failure factors of projects based on data mining to find problems with projects in an audit report, which is an objective project evaluation report. To do this, we identified the texts in the paragraph of suggestions about improvement. We made use of the superior classification algorithms in this study, which were NaiveBayes, SMO and J48. They were evaluated in terms of data of Recall and Precision after performing 10-fold-cross validation. In the identified texts, the failure factors of projects were analyzed so that they could be utilized in project implementation.

• Annual Conference on Human and Language Technology
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• 2001.10d
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• pp.130-137
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• 2001

최대 엔트로피 모델(maximum entropy model)은 여러 가지 자연언어 문제를 학습하는데 성공적으로 적용되어 왔지만, 두 가지의 주요한 문제점을 가지고 있다. 그 첫번째 문제는 해당 언어에 대한 많은 사전 지식(prior knowledge)이 필요하다는 것이고, 두번째 문제는 계산량이 너무 많다는 것이다. 본 논문에서는 텍스트 단위화(text chunking)에 최대 엔트로피 모델을 적용하는 데 나타나는 이 문제점들을 해소하기 위해 새로운 방법을 제시한다. 사전 지식으로, 간단한 언어 모델로부터 쉽게 생성된 결정트리(decision tree)에서 자동적으로 만들어진 규칙을 사용한다. 따라서, 제시된 방법에서의 최대 엔트로피 모델은 결정트리를 보강하는 방법으로 간주될 수 있다. 계산론적 복잡도를 줄이기 위해서, 최대 엔트로피 모델을 학습할 때 일종의 능동 학습(active learning) 방법을 사용한다. 전체 학습 데이터가 아닌 일부분만을 사용함으로써 계산 비용은 크게 줄어 들 수 있다. 실험 결과, 제시된 방법으로 결정트리의 오류의 수가 반으로 줄었다. 대부분의 자연언어 데이터가 매우 불균형을 이루므로, 학습된 모델을 부스팅(boosting)으로 강화할 수 있다. 부스팅을 한 후 제시된 방법은 전문가에 의해 선택된 자질로 학습된 최대 엔트로피 모델보다 졸은 성능을 보이며 지금까지 보고된 기계 학습 알고리즘 중 가장 성능이 좋은 방법과 비슷한 성능을 보인다 텍스트 단위화가 일반적으로 전체 구문분석의 전 단계이고 이 단계에서의 오류가 다음 단계에서 복구될 수 없으므로 이 성능은 텍스트 단위화에서 매우 의미가 길다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.411-412
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• 2010

전통적으로 자연어 텍스트로부터의 감정인식 연구는 감정 키워드에 기반한다. 그러나 감정 키워드만을 이용하면 자연어 문장이 원래 갖고 있는 통사정보나 의미정보는 잃어버리게 된다. 이를 극복하기 위해 본 논문에서는 자연어 텍스트를 Predicate Logic 형태로 변환하여 감정 정보처리의 기반데이터로 사용한다. Predicate Logic형태로 변환하기 위해서 의존 문법 구문분석기를 사용하였다. 이렇게 생성된 Predicate 데이터 중 감정 정보를 갖고 있는 Predicate만을 찾아내는데 이를 위해 Emotional Predicate Dictionary를 구축하였고 이 사전에는 하나의 Predicate마다 미리 정의된 개념 클래스로 사상 시킬 수 있는 정보를 갖고 있다. 개념 클래스는 감정정보를 갖고 있는지, 어떤 감정인지, 어떤 상황에서 발생하는 감정인지에 대한 정보를 나타낸다. 자연어 텍스트가 Predicate으로 변환되고 다시 개념 클래스로 사상되고 나면 KBANN으로 구현된 Lazarus의 감정 생성 규칙에 적용시켜 최종적으로 인식된 감정을 판단한다. 실험을 통해 구현된 시스템이 인간이 인식한 감정과 약 70%이상 유사한 인식 결과를 나타냄을 보인다.

• Journal of Digital Convergence
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• v.16 no.2
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• pp.195-201
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• 2018

Smartphones and PCs have become essential components of our daily life. People are expressing their opinions freely in SNS by using these devices. We are able to predict public opinions on specific subject by analyzing the related big data in SNS. In this paper, we have collected opinion data in SNS and analyzed reputation by text mining in order to make a prediction for the will of the people before 19th presidential election in South Korea. The result shows that our method makes more accurate estimate than other election polls.

• Journal of The Korean Association of Information Education
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• v.25 no.4
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• pp.633-640
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• 2021

Education related issues are social problems in which various groups and situations are intricately linked to each other. It is difficult to find issues by analyzing social phenomena related to education. Korean based text analysis can be analyzed in a quantitative. With the development of text analysis techniques, research results have been recently achieved, and it can be fully utilized to derive educational issues from text data in Korean. In this study, petition articles in the field of childcare/education were collected on the online-board of the Blue House National Petition website, and text analysis was used to derive issues in the education world. The analysis derived 6 topics through Latent Dirichlet Allocation(LDA) among topic modeling techniques. The association rules of major keywords were analyzed and visualized as graphs. In addition to deriving educational issues through the existing questionnaire, it can provide implications for future research directions and policies in that issues can be sufficiently discovered through text-based analysis methods.