• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.1071-1074
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• 2009

In a pre-existing online newspaper has inconvenience such that a difference of a newspaper from other newspapers is confirmed on another script window. To solve this inconvenience, we propose a system called Rose News Reader. Since our proposed system classifies the articles with respect to the publishers and domain, a user can compare the articles from diverse publisher and analyze them to obtain correct facts.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1483-1498
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• 2022

While public interests in the metaverse are growing recently in the Korean media and research, its understanding has not been fully established yet. In this study, we aimed to probe whether the rapid growth in media attention about the metaverse has increased its usage as a buzzword accompanied by an absence of scientific context. We analyzed publications and online news containing "metaverse" from 2020 to 2022. The data analysis methods are 1) time series frequency, 2) keyword network, 3) natural language model. The findings indicate the perception gap about metaverse between research and news articles broadened as its popularity has grown. Research about metaverse gradually expanded its connections with related topics-virtual and augmented realities-focusing on social changes in a remote environment. However, media reporting frequently used "metaverse" as a buzzword rather than explaining its scientific background, stimulating the proliferation of related topics and the dispersion of news content. This study further discusses the need for a media strategy to improve public conception of the long-term development of the metaverse.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.1
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• pp.109-121
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• 2022

Purpose - This study attempted to analyze news big data in order to examine the trend of change in housework due to technological innovation and family changes. Research design, data, and methodology - News big data was collected from Bigkinds for the purpose of trend analysis. A total of 8,270 articles containing 'housework' were extracted from news articles between January 1, 1990 and December 31, 2021. 11 general daily newspapers and 8 business newspapers were selected and were analyzed by dividing them into five-year units. Result - The change of trends in housework that appeared through news big data analysis can be summarized as below. First, the tendency to regard housework as work of women or housewives is gradually weakening. Instead, the centrality of connection with double income is increasing. Second, there is a tendency to strengthen the institutional approach to evaluation of the productivity of housework. Third, the possibility of market substitution for housework is expanding. Conclusion - In the era of the 4th industrial revolution, examining the impact of technological innovation and family change on housework not only enables the prospect of an industry, but also provides implications for policies related to housework. In addition, this study is differentiated in that it contributed to expand the field of housework research previously limited to analyzing survey data.

• 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 People, Plants, and Environment
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• v.24 no.6
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• pp.563-572
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• 2021

Background and objective: The ongoing COVID-19 pandemic restricted daily life, forcing people to spend time indoors. With the growing interest in mental health issues and residential environments, 'pet plants' have been receiving attention during the unprecedented social distancing measures. This study aims to analyze the change in trends of pet plants before and during the COVID-19 pandemic and provide basic data for studies related to pet plants and directions of future development. Methods: A total of 2,016 news articles using the keyword 'pet plants' were collected on Naver News from January 1, 2018 to August 15, 2019 (609 articles) and January 1, 2020 to August 15, 2021 (1,407 articles). The texts were tokenized into words using KoNLPy package, ultimately coming up with 63,597 words. The analyses included frequency of keywords and topic modeling based on Latent Dirichlet Allocation (LDA) to identify the inherent meanings of related words and each topic. Results: Topic modeling generated three topics in each period (before and during the COVID-19), and the results showed that pet plants in daily life have become the object of 'emotional support' and 'healing' during social distancing. In particular, pet plants, which had been distributed as a solution to prevent solitary deaths and depression among seniors living alone, are now expanded to help resolve the social isolation of the general public suffering from COVID-19. The new term 'plant butler' became a new trend, and there was a change in the trend in which people shared their hobbies and information about pet plants and communicated with others in online. Conclusion: Based on these findings, the trend data of pet plants before and after the outbreak of COVID-19 can provide the basis for activating research on pet plants and setting the direction for development of related industries considering the continuous popularity and trend of indoor gardening and green hobby.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• The Journal of the Korea Contents Association
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• v.17 no.8
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• pp.312-324
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• 2017

This study examined how German newspapers and magazines report Korean mass culture, specifically what content on Hallyu (Korean wave) was present and what news frames were used. External characteristics, the content attributes, report trend, and news frames on Hallyu in the German media were analyzed. According to the results, German media from 2009 to 2014 reported the least about Korean wave with only a total of 78 articles. The greatest amount of articles occurred in 2012 when by PSY was a global success. The most common Korean wave articles focused on film and K-pop, but not drama. Hallyu news showed mostly a positive trend. Additionally, a thematic frame was used. Content revealed that a culturally interesting frame was most common. In order to improve the Korea image in Germany, it is proposed that 'the mediated Culture PR' be adapted to German media.

• Journal of the Korean Society of Costume
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• v.22
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• pp.23-44
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• 1994

The purpose of this study was to clarify the cultural characteristic of dress and adornments by examining articles on dress adornments and related items in Korean newspaper over periods historically and objectively by means of content analysis. This study attempted to at-tain a macro-cultural view by analysing to at-tain a macro-cultural view by analysing closely the cultural characteristics of dress and adornments as a micro-cultural system through culturally based model suggested by hamilton. The two-hundreds and eighty articles on dress and adornments were selected from newspapers(most by form Chosun Ilbo and partly from Maeil Shinbo) pulished between 1920 and 1990. The results were as follows: 1. The culture of dress and adornment received much attention during the 1930's and 1960's and little during 1950's. 2. Various cultural characteristics of dress and adornments appeared on and after 1960's: reporting more foreign news items showing foreign-oriented and future-oriented features showing cultural relativism. In the 1920's and 1970's the contents of news items on dress and adornments show the most common- mass- oriented character. Foreign-oriented cultural tendencies in cloth-ing were increasing during from 1960's to 1970's but the tendencies were turned to rather tradition-oriented features on and after 1980's compare with 1960's-1970's. Advisory critical articles on dress and adornments were small in number and insignificant but compare with other periods these received much atten-tion during the 1920's and 1980's. 3. Ideological components received much at-tention on and after 1920's to 1990. Techo-nological components received much attention during 1920's and little during 1960's. The social structural components received a little attention on and after 1920's-1940's and 1990. 4. News items on women's dress and adornments received much attention from the 1920's to 1960's and news items on both men's and women's dress and adorments were in-creasing and received much attention on and after the 1970's. 5. The pragmatic cultures were mostly re-lated to techonological components and evaluative-normative culture were mostly re-lated to ideological and social structural components. In the light of these results dress and adorments as a cultural sub-system comprise a dynamic inteacting system that articulated directly with the macro-cultural system.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.221-233
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• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

• Smart Media Journal
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• v.7 no.1
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• pp.45-51
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• 2018

Avian influenza (AI) is notorious for its rapid infection rate, and has a serious impact on consumers and producers alike, especially in poultry farms. The AI outbreak, which occurred nationwide at the end of 2016, devastated the livestock farming industries. As a result, the prices of eggs and egg products had skyrocketed, and the event was reported by the media with heavy emphasis. The purpose of this study was to investigate the correlation between the egg price fluctuation and the keyword changes in online news articles reflecting social issues. To this end, we analyzed 682 cases of AI-related online news articles for fourteen weeks from November 2016 in South Korea. The results of this study are expected to contribute to understanding the relationship between the actual price of eggs and the keywords from news articles related to social issues.