• The Journal of the Convergence on Culture Technology
• /
• v.7 no.1
• /
• pp.432-438
• /
• 2021

This dissertation's object is to confirm the drastic popularity of La Peste of Albert Camus in Korea post-corona society using big data as the mean of inductive research. Analyzing news articles concerning Camus and investigating word frequency of the book La Peste will affirm the implications La Peste has on current Korea society as the outbreak spreads. As an analysis tool, Bigkinds of Korea Press Foundation and Nuagedemots, the French version of Word Cloud were used. For the past 30 years, Albert Camus has been known in Korea as the writer of L'étranger, but after the epidemic, he earned more reputation with La Peste. Compared to L'étranger that rebelled against the world's absurdity with ennui, La peste emphasizes the importance of resistance accompanied by solidarity. La peste conveys hope by depicting disastrous situations of citizens who confront the plague by organizing a health college. The novel delivers a lot of ethical inspiration to humanity in this exceptional circumstance of COVID-19.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.2
• /
• pp.343-348
• /
• 2022

This study aims to analyze the user experience of unmanned checkout counters perceived by consumers using SNS big data. For this study, blogs, news, intellectuals, cafes, intellectuals (tips), and web documents were analyzed on Naver and Daum, and 'unmanned checkpoints' were used as keywords for data search. The data analysis period was selected as two years from January 1, 2020 to December 31, 2021. For data collection and analysis, frequency and matrix data were extracted through Textom, and network analysis and visualization analysis were conducted using the NetDraw function of the UCINET 6 program. As a result, the perception of the checkout counter was clustered into accessibility, usability, continuous use intention, and others according to the definition of consumers' experience factors. From a supplier's point of view, if unmanned checkpoints spread indiscriminately to solve the problem of raising the minimum wage and shortening working hours, a bigger employment problem will arise from a social point of view. In addition, institutionalization is needed to supply easy and convenient unmanned checkout counters for the elderly and younger generations, children, and foreigners who are not familiar with unmanned calculation.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.32 no.4
• /
• pp.5-24
• /
• 2021

The purpose of this study was to investigate and analyze the operating conditions of lecture contents in contactless online classes for University students. First, as a result of analyzing the responses of 93 respondents, 93.3% of the respondents took real-time online lectures (47.7%) or recorded video lectures (45.6%). Second, as a result of analyzing the contents used as textbooks, it was found that e-books (materials) and paper books (materials) were used together (36.6%), or e-books or electronic materials (36.6% and 37.6% respectively) were used in both liberal arts (47.3%) and major subjects (39.8%). In addition to textbooks, both major subjects and liberal arts highly used web materials (47.6% and 40.5% respectively) and YouTube materials (33.3% and 48.0% respectively) as external materials. Third, both liberal arts and major subjects used 'electronic files in the form of PPT or text organized and written by instructors' (62.9% and 58.1% respectively), 'internet materials' (16.7% and 19% respectively) and 'paper book or materials' (10.4% and 12.3% respectively) to share lecture contents. For the screen displayed lecture contents, 93.5% of the respondents satisfied in major subjects, and 90.2% of the respondents satisfied in liberal arts. These results suggest developing multimedia-based lecture contents and an evaluation solution capable of real-time exam supervision, developing a task management system capable of AI-based plagiarism search, task guidance, and task evaluation, and institutionalizing a solution to copyright problems for electronicizing lecture materials so that lectures can be given in the ubiquitous environment.

• Journal of Korea Water Resources Association
• /
• v.55 no.spc1
• /
• pp.1261-1270
• /
• 2022

The purpose of this study is to collect and analyze information related to water leaks that cannot be easily accessed, and utilized by using the news search results that people can easily access. We applied a web crawling technique for extracting big data news on water leakage accidents in the water supply system and presented an algorithm in a procedural way to obtain accurate leak accident news. In addition, a data analysis technique suitable for water leakage accident information analysis was developed so that additional information such as the date and time of occurrence, cause of occurrence, location of occurrence, damaged facilities, damage effect. The primary goal of value extraction through big data-based leak analysis proposed in this study is to extract a meaningful value through comparison with the existing waterworks statistical results. In addition, the proposed method can be used to effectively respond to consumers or determine the service level of water supply networks. In other words, the presentation of such analysis results suggests the need to inform the public of information such as accidents a little more, and can be used in conjunction to prepare a radio wave and response system that can quickly respond in case of an accident.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.4
• /
• pp.299-314
• /
• 2017

Technological advances in media have expanded users' consciousness. At the same time, users have changed from passive into active voice by interacting media. The emergence of mobile made different structures and contents compared to the past. Especially, Korean culture of mobile converted original media channels to contents in a category. Plus, the usage structure of internet of this time converges in massive portal sites. It is because that the structure has aspect of emitting through remediation in the sites. Also, Korean massive portal sites have provided specific service named 'real-time issues'. This is not only the unique way of offering information that exists in Korea but also high usability of getting issues. We therefore considered the meaning of durability of real-time issues in the view of journalism, compared original media channels. Then, this paper identified the user attributes for acquisition of information following ways using informal and formal data from Korean massive portal sites named 'Daum' and 'Naver'.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.167-181
• /
• 2023

This study utilized TEXTOM, a social network analysis program to analyze changes in current tourism behavior after travel restrictions were eased after the outbreak of COVID-19. Data on the keywords 'domestic travel' and 'overseas travel' were collected from blogs, cafes, and news provided by Naver, Google, and Daum. The collection period was set from April to December 2022 when social distancing was lifted, and 2019 and 2020 were each set as one year and compared and analyzed with 2022. A total of 80 key words were extracted through text mining and centrality analysis was performed using NetDraw. Finally, through the CONCOR, the correlated keywords were clustered into 4. As a result of the study, tourism behavior in 2022 shows tourism recovery before the outbreak of COVID-19, segmentation of travel based on each person's preferred theme, prioritization of each country's corona mitigation policy, and then selecting a tourist destination. It is expected to provide basic data for the development of tourism marketing strategies and tourism products for the newly emerging tourism ecosystem after COVID-19.

• Smart Media Journal
• /
• v.12 no.8
• /
• pp.9-17
• /
• 2023

The development of information and communication technology and the improvement of the development and dissemination of smart devices have caused changes in the form of tourism, and the concept of smart tourism has since emerged. In this regard, researches related to smart tourism has been conducted in various fields such as policy implementation and surveys, but there is a lack of research on application reviews. This study collects Trip Advisor application review data in the Google Play Store to identify usage of the application and user satisfaction through Latent Dirichlet Allocation (LDA) topic modeling. The analysis results in four topics, two of which are positive and the other two are negative. We found that users were satisfied with the application's recommendation system, but were dissatisfied when the filters they set during search were not applied or that reviews were not published after updates of the application. We suggest more categories can be added to the application to provide users with different experiences. In addition, it is expected that user satisfaction can be improved by identifying problems within the application, including the filter function, and checking the application environment and resolving the error occurring during the application usage.

• Economic and Environmental Geology
• /
• v.56 no.4
• /
• pp.457-473
• /
• 2023

Asbestos has been produced, imported and used in various industries in Korea over the past decades. Since asbestos causes fatal diseases such as malignant mesothelioma and lung cancer, the use of asbestos has been generally banned in Korea since 2009. However, there are still many asbestos-containing materials around us, and safe management is urgently needed. This study aims to examine asbestos-related trend changes using major asbestos-related keywords based on the asbestos trend analysis using big data for the past 32 years (1991 to 2022) in Korea. In addition, we reviewed both domestic trends related to the production, import, and use of asbestos before 1990 and asbestos-related policies from 2023 to 2027. From 1991 to 2000, main keywords related to asbestos were research, workers, carcinogens, and the environment because the carcinogenicity of asbestos was highlighted due to domestic production, import, and use of asbestos. From 2001 to 2010, the main keywords related to asbestos were lung cancer, litigation, carcinogens, exposure, and companies because lawsuits were initiated in the US and Japan in relation to carcinogenicity due to asbestos. From 2011 to 2020, the high ranking keywords related to asbestos were carcinogen, baseball field, school, slate, building, and abandoned asbestos mine due to the seriousness of the asbestos problem in Korea. From 2021 to present (2023), the main search keywords related to asbestos such as school, slate (asbestos cement), buildings, landscape stone, environmental impact assessment, apartment, and cement appeared.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.37-51
• /
• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.197-218
• /
• 2019

In addition to economic growth and national income increase, China is also experiencing rapid growth in consumption of cosmetics. About 67% of the total trade volume of Chinese cosmetics is made by e-commerce and especially K-Beauty products, which are Korean cosmetics are very popular. According to previous studies, 80% of consumer goods such as cosmetics are affected by the word of mouth information, searching the product information before purchase. Mostly, consumers acquire information related to cosmetics through comments made by other consumers on SNS such as SINA Weibo and Wechat, and recently they also use information about beauty related video channels. Most of the previous online word-of-mouth researches were mainly focused on media itself such as Facebook, Twitter, and blogs. However, the informational characteristics and the expression forms are also diverse. Typical types are text, picture, and video. This study focused on these types. We analyze the unstructured data of SINA Weibo, the SNS representative platform of China, and Meipai, the video platform, and analyze the impact of K-Beauty brand sales by dividing online word-of-mouth information with quantity and direction information. We analyzed about 330,000 data from Meipai, and 110,000 data from SINA Weibo and analyzed the basic properties of cosmetics. As a result of analysis, the amount of online word-of-mouth information has a positive effect on the sales of cosmetics irrespective of the type of media. However, the online videos showed higher impacts than the pictures and texts. Therefore, it is more effective for companies to carry out advertising and promotional activities in parallel with the existing SNS as well as video related information. It is understood that it is important to generate the frequency of exposure irrespective of media type. The positiveness of the video media was significant but the positiveness of the picture and text media was not significant. Due to the nature of information types, the amount of information in video media is more than that in text-oriented media, and video-related channels are emerging all over the world. In particular, China has made a number of video platforms in recent years and has enjoyed popularity among teenagers and thirties. As a result, existing SNS users are being dispersed to video media. We also analyzed the effect of online type of information on the online cosmetics sales by dividing the product type of cosmetics into basic cosmetics and color cosmetics. As a result, basic cosmetics had a positive effect on the sales according to the number of online videos and it was affected by the negative information of the videos. In the case of basic cosmetics, effects or characteristics do not appear immediately like color cosmetics, so information such as changes after use is often transmitted over a period of time. Therefore, it is important for companies to move more quickly to issues generated from video media. Color cosmetics are largely influenced by negative oral statements and sensitive to picture and text-oriented media. Information such as picture and text has the advantage and disadvantage that the process of making it can be made easier than video. Therefore, complaints and opinions are generally expressed in SNS quickly and immediately. Finally, we analyzed how product diversity affects sales according to online word of mouth information type. As a result of the analysis, it can be confirmed that when a variety of products are introduced in a video channel, they have a positive effect on online cosmetics sales. The significance of this study in the theoretical aspect is that, as in the previous studies, online sales have basically proved that K-Beauty cosmetics are also influenced by word-of-mouth. However this study focused on media types and both media have a positive impact on sales, as in previous studies, but it has been proven that video is more informative and influencing than text, depending on media abundance. In addition, according to the existing research on information direction, it is said that the negative influence has more influence, but in the basic study, the correlation is not significant, but the effect of negation in the case of color cosmetics is large. In the case of temporal fashion products such as color cosmetics, fast oral effect is influenced. In practical terms, it is expected that it will be helpful to use advertising strategies on the sales and advertising strategy of K-Beauty cosmetics in China by distinguishing basic and color cosmetics. In addition, it can be said that it recognized the importance of a video advertising strategy such as YouTube and one-person media. The results of this study can be used as basic data for analyzing the big data in understanding the Chinese cosmetics market and establishing appropriate strategies and marketing utilization of related companies.