• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.59-76
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• 2023

This study examines critical issues and topics related to services using YouTube videos and research publications. We analyzed 2,853 YouTube videos and 19,973 research papers related to services, released during the 2013-June, 2023 period, using text mining and network analysis. In addition, the collected data was divided into pre- and post-COVID-19 pandemic periods to explore how key issues and topics regarding services have changed. These papers were sequentially analyzed through text mining and network construction and procedures. The results indicate that the central themes of YouTube videos were IT, data, and solution, while academic research focused on service quality, quality, and customer satisfaction. Regarding ego network analysis, the key issues in YouTube video contents revolved primarily around words related to the service industry. Although it was found that they generally lacked specific industry fields, academic papers explored diverse issues in various service fields. The results of this study can be utilized to understand changes in customer concerns in the service industry from practical and academic perspectives.

• Journal of Information Management
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• v.31 no.3
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• pp.1-20
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• 2000

A variety of trials selecting necessary and valuable information from rapidly increasing volume of data are made, and as one of them, data mining methods is an interest. This methodology is increasingly appzied to information management field which consists of efficient processing and systemizing increasing digital documents for user service. This article analyzes theoletical background and empirical case studies of data mining, and predicts the possibility of its application to information management area.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.3
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• pp.35-46
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• 2016

Social network services (SNS) that help to build relationship network and share a particular interest or activity freely according to their interests by posting comments, photos, videos,${\ldots}$ on online communities such as blogs have adopted and developed widely as a social phenomenon. Several researches have been done to explore the pattern and valuable information in social networks data via text mining such as opinion mining and semantic analysis. For improving the efficiency of text mining, keyword-based approach have been applied but most of researchers argued the limitations of the rules of Korean orthography. This research aims to construct a database of non-standard Korean words which are difficulty in data mining such abbreviations, slangs, strange expressions, emoticons in order to improve the limitations in keyword-based text mining techniques. Based on the study of subjective opinions about specific topics on blogs, this research extracted non-standard words that were found useful in text mining process.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.3
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• pp.87-97
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• 2018

The traffic accident reports that are generated by the Traffic Broadcasting Networks(TBN) are unstructured data. It, however, has the value as some sort of real-time traffic information generated by the viewpoint of the drives and/or pedestrians that were on the roads, the time and spots, not the offender or the victim who caused the traffic accidents. However, the traffic accident reports, which are big data, were not applied to traffic accident analysis and traffic related research commonly. This study adopting text-mining technique was able to provide a clue for utilizing it for the impacts of traffic accidents. Seven years of traffic reports were grasped by this analysis. By analyzing the reports, it was possible to identify the road names, accident spot names, time, and to identify factors that have the greatest influence on other drivers due to traffic accidents. Authors plan to combine unstructured accident data with traffic reports for further study.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.249-275
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• 2016

For the past 50 years, innovation field has gone through an evolution. The range of research topics on innovation has expanded and diversified, along with a quantitative increase. In a multi-disciplinary field like innovation, to explore new topics and understand research trends, it is necessary to possess a comprehensive understanding regarding the current status of, and trends in, the research. In this study, the research trend in innovation studies from 2000 to 2015 was analyzed in a holistic perspective. For this, a novel technique, text mining was used. The result shows that innovation studies has focused on the traditional and emerging topics. Also, the differentiations has appeared in some of the traditional topics. This study provides not only an understanding of research dynamics, but also an opportunity to gain insights into the evolution of a new paradigm from an academic perspective.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.26 no.1
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• pp.217-231
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• 2015

This study aims to infer the gene-protein 'brings_about' chains of pancreatic cancer which were referred to in the pancreatic cancer related researches by constructing the gene-protein interaction network of pancreatic cancer. The chains can help us uncover publicly unknown knowledge that would develop as empirical studies for investigating the cause of pancreatic cancer. In this study, we applied a novel approach that grafts text mining and the main path analysis into Swanson's ABC model for expanding intermediate concepts to multi-levels and extracting the most significant path. We carried out text mining analysis on the full texts of the pancreatic cancer research papers published during the last ten-year period and extracted the gene-protein entities and relations. The 'brings_about' network was established with bio relations represented by bio verbs. We also applied main path analysis to the network. We found the main direct 'brings_about' path of pancreatic cancer which includes 14 nodes and 13 arcs. 9 arcs were confirmed as the actual relations emerged on the related researches while the other 4 arcs were arisen in the network transformation process for main path analysis. We believe that our approach to combining text mining analysis with main path analysis can be a useful tool for inferring undiscovered knowledge in the situation where either a starting or an ending point is unknown.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1259-1269
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• 2015

Recently, big data is positioning as a keyword in the academic circles. And usefulness of big data is carried into government, a local public body and enterprise as well as academic circles. Also they are endeavoring to obtain useful information in big data. This research mainly deals with analyses of box office success or failure of films using text mining. For data, it used a portal site 'D' and film review data, grade point average and the number of screens gained from the Korean Film Commission. The purpose of this paper is to propose a model to predict whether a film is success or not using these data. As a result of analysis, the correct classification rate by the prediction model method proposed in this paper is obtained 95.74%.

• The Journal of Sustainable Design and Educational Environment Research
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• v.19 no.2
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• pp.11-20
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• 2020

The goal of this study was to investigate the implementation and related issues of the school space innovation issued by key Korean mass media using text mining. To accomplish this goal, this study collected 519 news articles associated with the school space innovation issued by 54 Korean mass media companies. Based on this data, this study performed the frequency analysis and network analysis regarding the keywords. Based on the findings, the characteristics of school space innovation are summarized as follows: First, school space innovation has progressed in response to future education. Second, users are actively participating in school space innovation. Third, experts are supporting the innovation of school space by establishing a cooperative system. Fourth, the community is actively considering the innovation of school space. Fifth, the main projects of the Ministry of Education and the Provincial Offices of Education are actively conducted in a mix of top-down and bottom-up approaches. The findings of this study will contribute to providing a clear direction for contemporary school space innovation and implications for future research agenda and implementation.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.223-228
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• 2020

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.764-770
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• 2019

In this research, text mining techniques were used to analyze the papers related to the "4th Industry". In order to analyze the papers, total of 685 papers were collected by searching with the keyword "4th industry" in Korea Journal Index(KCI) from 2016 to 2019. We used Python-based web scraping program to collect papers and use topic modeling techniques based on LDA algorithm implemented in R language for data analysis. As a result of perplexity analysis on the collected papers, nine topics were determined optimally and nine representative topics of the collected papers were extracted using the Gibbs sampling method. As a result, it was confirmed that artificial intelligence, big data, Internet of things(IoT), digital, network and so on have emerged as the major technologies, and it was confirmed that research has been conducted on the changes due to the major technologies in various fields related to the 4th industry such as industry, government, education field, and job.