• 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.

• Journal of the Korean Society for information Management
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• v.35 no.1
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• pp.231-250
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• 2018

In this paper, we propose a new methodology for extracting and formalizing subjective topics at a specific time using a set of keywords extracted automatically from online news articles. To do this, we first extracted a set of keywords by applying TF-IDF methods selected by a series of comparative experiments on various statistical weighting schemes that can measure the importance of individual words in a large set of texts. In order to effectively calculate the semantic relation between extracted keywords, a set of word embedding vectors was constructed by using about 1,000,000 news articles collected separately. Individual keywords extracted were quantified in the form of numerical vectors and clustered by K-means algorithm. As a result of qualitative in-depth analysis of each keyword cluster finally obtained, we witnessed that most of the clusters were evaluated as appropriate topics with sufficient semantic concentration for us to easily assign labels to them.

• Korean journal of communication and information
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• v.57
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• pp.49-68
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• 2012

Much research has been documented on the topic of journalist labour, including process of news production, organizational culture and journalist ethics. Relatively little interest is given to the recent changes in journalist labor caused by the digital convergence in contemporary news industry, however. This study explore the changes from the perspective of the transformation of professional works, practices and identities. For the purpose, in-depth interviews with 17 Korean journalists were conducted and the results can be summarized as follows. First, interviewed journalists expressed both expectations and worries concerning the changes of news production environments. Second, it was found that the new environments could conflict with the old professional culture and confuse the professional identities. Third, the confusion of professional identities was concerned with adaptability, emotional consent or resistance to the changes.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.47-54
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• 2019

This study confirms the polarity of news articles on apartment prices using Opinion Mining which has widely been used for a big data analysis. The analyses were carried out utilizing internet news articles posted on the Naver for two years: 2012 and 2018. We proposed a sentiment analysis model and modeled a topic-oriented sentiment dictionary construction methods. As a result of analyzing the proposed sentiment analysis model, it was confirmed that there was a difference according to the tendency of the media companies in selecting social issues at the time of rising apartment prices. At the same time, we were able to find more affirmative articles in the media companies which share similar sentiment with the government in charge. In this paper, we proposed a sentiment analysis model that can be used in real estate field and analyzed the polarity of unformatted data related to real estate. In order to integrate them into various fields in the future, it is necessary to build the sentiment dictionaries by themes, as well as to collect various unformatted data over extended periods.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.145-155
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• 2022

North Korean defectors are experiencing economic hardship due to the prolonged COVID-19 outbreak. In order to solve this problem, interest in starting a business is increasing. This study targeted the current and previous government, and discovered major topics through text mining of news data on North Korean defector starting a business to examine the start-up support policies according to the keynote of the present regime. Additionally, key factors for successful start-ups were derived through interviews with North Korean defectors who have done them. As a result of the analysis, it is necessary to focus on women and the youth, and to actively expand specialized entrepreneurship education and financial support for North Korean defectors. In addition, it was confirmed that there is a need for a practical and continuous entrepreneurship education program.

• Journal of Science Education
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• v.45 no.3
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• pp.292-303
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• 2021

This study identifies the terms frequently used together with energy in online science news articles and topics of the news reports to find out how the term energy is used in everyday life and to draw implications for science curriculum and instruction about energy. A total of 2,171 online news articles in science category published by 11 major newspaper companies in Korea for one year from March 1, 2018 were selected by using energy as a search term. As a result of natural language processing, a total of 51,224 sentences consisting of 507,901 words were compiled for analysis. Using the R program, term frequency analysis, semantic network analysis, and structural topic modeling were performed. The results show that the terms with exceptionally high frequencies were technology, research, and development, which reflected the characteristics of news articles that report new findings. On the other hand, terms used more than once per two articles were industry-related terms (industry, product, system, production, market) and terms that were sufficiently expected as energy-related terms such as 'electricity' and 'environment.' Meanwhile, 'sun', 'heat', 'temperature', and 'power generation', which are frequently used in energy-related science classes, also appeared as terms belonging to the highest frequency. From a network analysis, two clusters were found including terms related to industry and technology and terms related to basic science and research. From the analysis of terms paired with energy, it was also found that terms related to the use of energy such as 'energy efficiency,' 'energy saving,' and 'energy consumption' were the most frequently used. Out of 16 topics found, four contexts of energy were drawn including 'high-tech industry,' 'industry,' 'basic science,' and 'environment and health.' The results suggest that the introduction of the concept of energy degradation as a starting point for energy classes can be effective. It also shows the need to introduce high-tech industries or the context of environment and health into energy learning.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.38-49
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• 2021

This study analyzed the trend of the term 'false and exaggerated advertisement' in 5,141 newspaper articles from 1990 to 2019 using text mining methodology. First of all, we identified the most frequent keywords of false and exaggerated advertisements through frequency analysis for all newspaper articles, and understood the context between the extracted keywords. Next, to examine how false and exaggerated advertisements have changed, the frequency analysis was performed by separating articles by 10 years, and the tendency of the keyword that became an issue was identified by comparing the number of academic papers on the subject of the highest keywords of each year. Finally, we identified trends in false and exaggerated advertisements based on the detailed keywords in the topic using the topic modeling. In our results, it was confirmed that the topic that became an issue at a specific time was extracted as the frequent keywords, and the keyword trends by period changed in connection with social and environmental factors. This study is meaningful in helping consumers spend wisely by cultivating background knowledge about unfair advertising. Furthermore, it is expected that the core keyword extraction will provide the true purpose of advertising and deliver its implications to companies and related employees who commit misconduct.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.365-385
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• 2022

The easy payment service is a payment and remittance service that uses a simple authentication method. As online transactions have increased due to COVID-19, the use of an easy payment service is increasing. At the same time, electronic financial industries such as Naver Pay, Kakao Pay, and Toss are diversifying the competition structure of the easy payment market; meanwhile overseas fintech companies PayPal and Alibaba have a unique market share in their own countries, while competition is intensifying in the domestic easy payment market, as there is no unique market share. In this study, the participants in the easy payment market were classified as electronic financial companies, mobile phone manufacturers, and financial companies, and a SWOT analysis was conducted on the representative services in each industry. The analysis examined the user reviews of Google Play Store via a topic modeling analysis, and it employed positive topics as strengths and negative topics as weaknesses. In addition, topic modeling was conducted by dividing news articles into political, economic, social, and technology (PEST) articles to derive the opportunities and threats to easy payment services. Through this research, we intend to confirm the service capabilities of easy payment companies and propose a service activation strategy that allows gaining the upper hand in the market.

• Information Systems Review
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• v.20 no.2
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• pp.139-161
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• 2018

As Korea became one of the oldest countries in the world, successful aging emerged as an important issue to individuals as well as to society. This study aims to determine not only the Korean seniors' happiness and unhappiness factors but also the means to enhance their happiness and deal with unhappiness. We collected news articles related to the happiness and unhappiness of seniors with nine keywords based on Alderfer's ERG Theory. We then applied a topic modeling technique, Latent Dirichlet Allocation, to examine the main issues underlying the seniors' happiness and unhappiness. According to the analysis, we investigated the conditions of happiness and unhappiness by inspecting the topics based on each keyword. We also conducted a detailed analysis based on the main factors from topic modeling. We proposed specific ways to increase and overcome the happiness and unhappiness of seniors, respectively, in terms of government, corporate, family, and other social welfare organizations. This study indicates the major factors that affect the happiness and unhappiness of seniors. Specific methods to boost happiness and relief unhappiness are suggested from the additional analysis.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.25-41
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• 2014

Recently, the number of social media users has increased rapidly because of the prevalence of smart devices. As a result, the amount of real-time data has been increasing exponentially, which, in turn, is generating more interest in using such data to create added value. For instance, several attempts are being made to analyze the relevant search keywords that are frequently used on new portal sites and the words that are regularly mentioned on various social media in order to identify social issues. The technique of "topic analysis" is employed in order to identify topics and themes from a large amount of text documents. As one of the most prevalent applications of topic analysis, the technique of issue tracking investigates changes in the social issues that are identified through topic analysis. Currently, traditional issue tracking is conducted by identifying the main topics of documents that cover an entire period at the same time and analyzing the occurrence of each topic by the period of occurrence. However, this traditional issue tracking approach has two limitations. First, when a new period is included, topic analysis must be repeated for all the documents of the entire period, rather than being conducted only on the new documents of the added period. This creates practical limitations in the form of significant time and cost burdens. Therefore, this traditional approach is difficult to apply in most applications that need to perform an analysis on the additional period. Second, the issue is not only generated and terminated constantly, but also one issue can sometimes be distributed into several issues or multiple issues can be integrated into one single issue. In other words, each issue is characterized by a life cycle that consists of the stages of creation, transition (merging and segmentation), and termination. The existing issue tracking methods do not address the connection and effect relationship between these issues. The purpose of this study is to overcome the two limitations of the existing issue tracking method, one being the limitation regarding the analysis method and the other being the limitation involving the lack of consideration of the changeability of the issues. Let us assume that we perform multiple topic analysis for each multiple period. Then it is essential to map issues of different periods in order to trace trend of issues. However, it is not easy to discover connection between issues of different periods because the issues derived for each period mutually contain heterogeneity. In this study, to overcome these limitations without having to analyze the entire period's documents simultaneously, the analysis can be performed independently for each period. In addition, we performed issue mapping to link the identified issues of each period. An integrated approach on each details period was presented, and the issue flow of the entire integrated period was depicted in this study. Thus, as the entire process of the issue life cycle, including the stages of creation, transition (merging and segmentation), and extinction, is identified and examined systematically, the changeability of the issues was analyzed in this study. The proposed methodology is highly efficient in terms of time and cost, as it sufficiently considered the changeability of the issues. Further, the results of this study can be used to adapt the methodology to a practical situation. By applying the proposed methodology to actual Internet news, the potential practical applications of the proposed methodology are analyzed. Consequently, the proposed methodology was able to extend the period of the analysis and it could follow the course of progress of each issue's life cycle. Further, this methodology can facilitate a clearer understanding of complex social phenomena using topic analysis.