• Journal of Digital Convergence
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• v.16 no.8
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• pp.129-136
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• 2018

This study analyzed technology application field and technology transfer type related to the 4th industrial revolution using frequency, visualization, and association analysis of text mining of Big Data. The analysis was conducted between the last three years (2015 - 2017) registered with the NTB of KIAT transfer technology database was utilized. As a result of analysis, First, First, transfer technologies called core technologies of the Fourth Industrial Revolution are a lot of about robots, 3D, autonomous driving, and wearables. Second, as the year go by, transfer technolgy registration such as IoT, Cloud, VR is increasing. Third, the results of the association analysis of technology transfer type are as follows. IoT and VR showed preference for technology trading and licensing, autonomous driving technology trading, wearable licensing, robots preferring technology cooperation, licensing, and technology trading.

• The Journal of Bigdata
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• v.1 no.2
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• pp.21-34
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• 2016

There are various investment strategy reports available online, prepared by many financial analysts. If the correlation between the title of the report and analyst forecast can be found, we can tell from the title whether analyst' forecast will be reliable or not. The objective of this study is to see the correlation between the title of analyst investment strategy report and the actual result of forecast by using the Text Mining technique. The result of actual analysis showed that "strong buy and sell call" appeared in the title lead the higher accuracy of analyst forecast and fulfillment ratio. The results that potential investors can get better information by reading the title of the analyst report. We hope that this study could be the basis for new methodologies in this area.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.474-485
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• 2017

Sungnyemun Gate, Korea's National Treasure No.1, was destroyed by fire on February 10, 2008 and has been re-opened to the public again as of May 4, 2013 after a reconstruction work. Sungnyemun Gate become a national issue and draw public attention to be a major topic on news or research. In this research, text mining and association rule mining techniques were used on keyword of newspaper articles related to Sungnyemun Gate as a cultural heritage from 2002 to 2016 to find major keywords and keyword association rule. Next, we analyzed some typical and specific keywords that appear frequently and partially depending on before and after the fire and newpaper companies. Through this research, the trends and keywords of newspapers articles related to Sungnyemun Gate could be understood, and this research can be used as fundamental data about Sungnyemun Gate to information producer and consumer.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.10-27
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• 2018

The purpose of this study is to explore the meaning of happiness and unhappiness in Korean society through text mining analysis. Similar words with keywords(happiness/unhappiness) from online news portal are extracted using Word2Vec and TF-IDF method. We also use the K-LIWC dictionary to perform the sentiment analysis of words associated with happiness and unhappiness. In TF-IDF analysis, happiness and unhappiness are highly related to social factors and social issues of the year. In Word2Vec analysis, 'Hope' has been similar with happiness for six years. In K-LIWC analysis, 'money/financial issues', 'school', 'communication' is highly related with happiness and unhappiness. In addition, 'physical condition and symptom' is highly related to unhappiness. Implications, limitations, and suggestions for future research are also discussed.

• 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%.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.231-243
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• 2017

The purpose of this study is to improve the social acceptance of AVs by analyzing the citizen's perception using an emotional analysis technique which belongs to a type of text mining. The source of the data is originated from 3 year accumulated internet articles and comments on AV from 164 newspapers and Naver. According to the study results, there exists a positive perception on AVs, although negative ones are more frequent than the positive. Also most of people take neutral position on AV due to the unfamiliarity and lack of experience on AVs And these problems needs to be responded before AV's commercialization through continuous analyses on the perception and social acceptance.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.160-173
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• 2021

Recently, research on the development of autonomous vehicles has increased worldwide. Moreover, a means to identify and analyze the characteristics of traffic accidents of autonomous vehicles is needed. Accordingly, traffic accident data of autonomous vehicles are being collected in California, USA. This research examined the characteristics of traffic accidents of autonomous vehicles. Primarily, traffic accident data for autonomous vehicles were analyzed, and the text data used text-embedding techniques to derive major keywords and four topics. The methodology of this study is expected to be used in the analysis of traffic accidents in autonomous vehicles.