• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2571-2586
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• 2022

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.214-215
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• 2022

This study analyzed the development stage and change management necessity of the construction IT system through existing research and literature review, and used WordCloud, one of the text mining techniques, to analyze current construction trends and major issues. The necessity of change management is derived by using existing research literature and construction-related social issues as analysis data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.471-492
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• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.113-124
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• 2015

Unlike structured data which are gathered and saved in a predefined structure, unstructured text data which are mostly written in natural language have larger applications recently due to the emergence of web 2.0. Text mining is one of the most important big data analysis techniques that extracts meaningful information in the text because it has not only increased in the amount of text data but also human being's emotion is expressed directly. In this study, we used R program, an open source software for statistical analysis, and studied algorithm implementation to conduct analyses (such as Frequency Analysis, Cluster Analysis, Word Cloud, Social Network Analysis). Especially, to focus on our research scope, we used keyword extract method based on a Data Dictionary. By applying in real cases, we could find that R is very useful as a statistical analysis software working on variety of OS and with other languages interface.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.72-82
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• 2016

Various studies have been done in medical service area but they have just focused on the examination of the relationships between cause and effect variables. This study, thus, empirically analyzed qualitative data regarding medical service problems using word cloud technique. The major results of the paper are as follows. The data reveal ten sources in medical service - forced treatment, excess inspection, misdiagnosis, carelessness, inexperienced service, waiting for emergency, reservation problem, unkindness, process problem, and inconvenience. Major words in the category of irrecoverable service failure are misdiagnosis, careless treatment, and inexperienced service whereas those in recoverable service failure are unkind attitude and negative experience in reservation system. Those who experienced a medical service problem are usually engaged in a public act and they make public protests and legal action against very severe problems. The conclusion of this study also suggests a summary, implication, and agenda of the research.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.141-146
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• 2018

The authors' author keywords are the most important elements that characterize the contents of the paper, By analyzing this in real time and providing it to users, It is possible to grasp research trends. Unstructured data of a journal created in a paper is constructed as a database, make use of this to make index data structure that can search in real time. In the index data structure, a thesis containing a specific keyword is searched, By extracting and clustering the author keywords, By presenting to the user a word cloud that can be displayed by size according to the weight, designed a method to visualize research trends. We also present the results of the research trend analysis of the keywords "virus" and "iris recognition" in the implemented system.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.53-59
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• 2017

This study was attempted to identify the domestic research related to depression and stress. The subjects of the analysis were 1,875 college degree theses thrown in the National Assembly Library searched by the depression and stress keyword as of November 30, 2016. The analysis method visualizes atypical data with Word Cloud, which is one of the text mining techniques. We also used the R'LDA package and LDA to classify treatment and subjects. As a result of the analysis, 233(12.4%) of the total papers with therapeutic keywords were found. Application of treatment methods was art therapy, music therapy, horticultural therapy, cognitive behavior therapy, clinical art therapy, cognitive therapy, psychological therapy, depression treatment, group therapy, laughter treatment sequence. The study subjects were adolescents, elderly, patient, mother, child, female, parents, and college students in order. The results of LDA topic analysis for adolescents were classified into four topics: self-support, treatment program, relationship effect, and variable study.

• Smart Media Journal
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• v.6 no.1
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• pp.47-53
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• 2017

Social issues are important factors that decide government policy and newspapers are critical channels that reflect them. Analysing news articles can contribute to understanding social issues, but it is very difficult to analyse the unstructured large volumes of news data manually. Therefore, this study aims to analyze the different views among stakeholders of a specific social issue by using text analysis, word cloud analysis and associative analysis methods, which systematically transform unstructured news data into structured one. We analyzed a total of 115 news articles and a total of 6,772 comments, collected from the selected newspapers (Chosun-Il-bo, Joongang-Il-bo, Donga-Il-bo, Maeil Newspaper, Busan-Il-bo) for two weeks. We found that there are significant differences in tone between newspapers. While nation-wide daily newspapers focus on political relations with local areas, local daily newspapers tend to write articles to represent local governments' interests.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.551-567
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• 2021

Purpose: The purpose of this paper is to present implications by analyzing research trends on smart factories by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on smart factories. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "SMART FACTORY" and "Smart Factory" as search terms, and the titles and Korean abstracts were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, 739 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; Smart factory research slowed down from 2005 to 2014, but until 2019, research increased rapidly. According to the analysis by fields, smart factories were studied in the order of engineering, social science, and complex science. There were many 'engineering' fields in the early stages of smart factories, and research was expanded to 'social science'. In particular, since 2015, it has been studied in various disciplines such as 'complex studies'. Overall, in keyword analysis, the keywords such as 'technology', 'data', and 'analysis' are most likely to appear, and it was analyzed that there were some differences by fields and years. Conclusion: Government support and expert support for smart factories should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to smart factories. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.135-146
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• 2019

This study was designed to analyze trends in the entire convergence research beyond academic research through social media big data analysis at a time when interdisciplinary convergence research is emphasized along with the fourth industrial revolution. For this purpose, about 150,000 cases of texts and titles were acquired for about 10 years from January 2009 to September 2018 in connection with the convergence research in social media, and word cloud and network analysis were conducted. As a results, the research fields that were actively conducted for each period were eco-tech in 2009 and 2010, smart technology in 2011 and 2012, information and communication in 2013 and 2014, robots in 2015 and 2016, and artificial intelligence in 2017 and 2018. Also, the research areas that have been consistently conducted for about 10 years are culture, design, chemistry, nanotechnology, biotechnology, robot, IT, and information and communication. Since this study identifies trends in convergence research over time, it can be helpful to researchers who are planning convergence research direction by understanding the trends of convergence research.