• Korean Journal of Schizophrenia Research
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• v.23 no.2
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• pp.58-64
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• 2020

Objectives: In this study, we conducted an exploratory analysis of the current media trends on schizophrenia using text-mining methods. Methods: First, web-crawling techniques extracted text data from 575 news articles in 10 major newspapers between 2018 and 2019, which were selected by searching "schizophrenia" in the Naver News. We had developed document-term matrix (DTM) and/or term-document matrix (TDM) through pre-processing techniques. Through the use of DTM and TDM, frequency analysis, co-occurrence network analysis, and topic model analysis were conducted. Results: Frequency analysis showed that keywords such as "police," "mental illness," "admission," "patient," "crime," "apartment," "lethal weapon," "treatment," "Jinju," and "residents" were frequently mentioned in news articles on schizophrenia. Within the article text, many of these keywords were highly correlated with the term "schizophrenia" and were also interconnected with each other in the co-occurrence network. The latent Dirichlet allocation model presented 10 topics comprising a combination of keywords: "police-Jinju," "hospital-admission," "research-finding," "care-center," "schizophrenia-symptom," "society-issue," "family-mind," "woman-school," and "disabled-facilities." Conclusion: The results of the present study highlight that in recent years, the media has been reporting violence in patients with schizophrenia, thereby raising an important issue of hospitalization and community management of patients with schizophrenia.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.814-820
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• 2009

With the rapid development of China economy, many engineering projects with large scale and investment were constructed in China and some were the biggest ones in the world. With the development of engineering practice, great progress in the research of engineering management of China was made and a large number of research findings were embodied in content of research papers and were represented by technical words. To know the state of arts in the research field of engineering management in China, three major parts, namely title, abstract and keywords of research papers in last five years from three representative Chinese journals about engineering management were chose as research materials. Unlike western languages, there are no delimiters between the words of Chinese, so the maximum matching and frequency statistics (MMFS) method, a text segmentation technique of text mining Chinese, was presented to extract the features consisting of technical words, phrases and words from the research materials. Recent research and developing trend of engineering management in China were found by comparing and analyzing the difference of technical words in the research materials of last five years.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1207-1217
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• 2014

2011 Korean Economic Census is the first economic census in Korea, which contains text data on menus served by Korean-food restaurants as well as structured data on characteristics of restaurants including area, opening year and total sales. In this paper, we applied text mining to the text data and investigated statistical and technical issues and characteristics of Korean text mining. Pork belly roast was the most popular menu across provinces and/or restaurant types in year 2010, and the number of restaurants per 10000 people was especially high in Kangwon-do and Daejeon metropolitan city. Beef tartare and fried pork cutlet are popular menus in start-up restaurants while whole chicken soup and maeuntang (spicy fish stew) are in long-lived restaurants. These results can be used as a guideline for menu development to restaurant owners, and for government policy-making process that lead small restaurants to choose proper menus for successful business.

• Journal of dental hygiene science
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• v.21 no.1
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• pp.70-78
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• 2021

Background: The aim of this study was to analyze the public demand for information about the job of dental hygienists by mining text data collected from the online Q & A section on an Internet portal site. Methods: Text data were collected from inquiries that were posted on the Naver Q & A section from January 2003 to July 2020 using "dental hygienist job recognition," "role recognition," "medical assistance," and "scaling" as search keywords. Text mining techniques were used to identify significant Korean words and their frequency of occurrence. In addition, the association between words was analyzed. Results: A total of 10,753 Korean words related to the job of dental hygienists were extracted from the text data. "Chi-lyo (treatment)," "chigwa (dental clinic)," "ske-illing (scaling)," "itmom (gum)," and "chia (tooth)" were the five most frequently used words. The words were classified into the following areas of job of the dental hygienist: periodontal disease treatment and prevention, medical assistance, patient care and consultation, and others. Among these areas, the number of words related to medical assistance was the largest, with sixty-six association rules found between the words, and "chi-lyo," "chigwa," and "ske-illing" as core words. Conclusion: The public demand for information about the job of dental hygienists was mainly related to "chi-lyo," "chigwa," and "ske-illing" as core words, demonstrating that scaling is recognized by the public as the job of a dental hygienist. However, the high demand for information related to treatment and medical assistance in the context of dental hygienists indicates that the job of dental hygienists is recognized by the public as being more focused on medical assistance than preventive dental care that are provided with job autonomy.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4090-4102
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• 2018

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.52-59
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• 2014

We survey 3,857 journal articles published on the four domestic academic journals in the industrial engineering field during 1975~2012. Titles, abstracts, and keywords of the papers are searched by means of text mining technique to draw the information on the methodologies and techniques adopted in the papers, and then we aggregate and merge similar ones to obtain final 38 representative methodologies and techniques. Trends of these methodologies and techniques are studied by analyzing frequencies, clustering, and finding association rules among them. Results of the paper can shed a light to choose tools in the future education and research in the industrial engineering related area.

• 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.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.183-190
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• 2019

In the era of the Fourth Industrial Revolution, digital transformation, which means changes in all industrial structures, politics, economics and society as well as IT technology, is an important issue. It is difficult to know which research topic is being studied because digital transformation is being studied in various fields. Convergence research is possible because a research topic is studied in various fields such as computer science area and Decision science area. However, it is difficult to know the specific research status of the research topic. In this study, eight research topics were derived using the topic modeling technique of text mining for abstract of academic literature and the trend of each topic was analyzed. We also proposed to create a Topic-Word Proportions Table in the LDA based Topic modeling process to predict the topic of new literature. The results of this study are expected to contribute to advanced convergence research on topic of digital transformation. It is expected that the literature related to each research topic will be grasped and contribute to the design of a new convergence research.

• Educational Technology International
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• v.23 no.1
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• pp.97-127
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• 2022

With the recent advancement of technology, there has been an increase in professional development activities, including teachers using blogs to share practical knowledge and reflect on teaching and learning. This study was conducted to identify the contents of practical knowledge shared through the K-12 teachers' blogs. To achieve the research objective, 70,571 blog posts were collected from 329 blogs of K-12 teachers in Korean and analyzed using text mining techniques. The results of the study are as follows. First, practical knowledge sharing activities using teacher blogs have increased. Teachers posted a lot of blogs during the semester. Second, primary school teachers share various curriculum activities, reflections on project classes, class management, opinions related to education, and personal. Third, secondary school teachers share summaries and reviews of curriculum, materials related to college entrance exams, various instructional materials, opinions related to education, and personal experiences on their blogs. This study suggested that blogs are widely used as a venue for sharing practical knowledge of teachers, and that blogs can be a useful way to develop professionalism.