• Journal of The Korean Association For Science Education
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• v.37 no.6
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• pp.1005-1014
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• 2017

Focusing on the role of the science museum as an institution to improve the scientific literacy of the public, this study investigated public perception and behavior about science museum to know how much science museums affect the public by using social media big data analysis. For this purpose, we extracted texts containing 'science museum' in Naver blogs and Twitter, analyzed them by using network, frequency, co-ocurrence, and semantics analysis and compared them with the results in English speaking countries. As a result, blogs were mainly concerned with science museum among parents who have young children, while in Twitter posts from many students who visited as a group appeared. Therefore, the Korean public used science museum mainly as a space for children's experience, and in this case, programs and exhibitions of science museums are perceived positively. On the other hand, students who visited as a group showed some negative emotions. The result of comparison with the cases of foreign countries in terms of the function of the third generation science museum such as communications with the science museum and the public and the participation of the public in science, the Korean public hardly mentioned the scientific contents, words related to communications such as 'argue', and curators or staff after visiting the science museum. In contrast to many verbs related to meaningful activities such as 'learn', 'participate', 'listen', 'read', 'ask', 'think' appeared in English, only a small number of verbs include 'ask' and 'thin' appeared in Korean. Therefore, science museum need to improve impression, communicating with public, and involving activity with impact and variety after visit.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.209-215
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• 2022

In this study, detailed factors of public sentiment toward the 'post-corona new normal' were examined through social media big data sentiment analysis. Thus, it is to provide basic data to preemptively cope with the post-COVID-19 era. For data collection and analysis, the emotional analysis program of 'Textom', a big data analysis program, was used. The data collection period is one year from October 5, 2020 to October 5, 2021, and the collection channels are set as blogs, cafes, Twitter, and Facebook on Daum and Naver. The original data edited and refined a total of 3,770 collected texts from this channel were used for this study. The conclusion is as follows. First, there is a high level of interest and liking for the 'post-corona new normal'. In other words, it can be seen that optimism such as daily recovery, technological growth, and expectations for a new future took the lead at 77.62%. Second, negative emotions such as sadness and rejection are 22.38% of the total, but the intensity of emotions is 23.91%, which is higher than the ratio, suggesting that these negative emotions are intense. This study has a contribution to the detailed factor analysis of the public's positive and negative emotions through big data analysis on the 'post-corona new normal'.

• Journal of Cadastre & Land InformatiX
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• v.49 no.2
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• pp.67-82
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• 2019

The purpose of this study is to analyze the issues and perceptions of smart cities. First, based on the big data analysis platform, big data analysis on smart cities were conducted to derive keywords by year, word cloud, and frequency of generation of smart city keywords by time. Second, trend and flow by area were analyzed by reclassifying major keywords by year based on meta-keywords. Third, emotional recognition flow for smart cities and major emotional keywords were derived. While U-City in the past is mostly centered on creating infrastructure for new towns, recent smart cities are focusing on sustainable urban construction led by citizens, according to the analysis. In addition, it was analyzed that while infrastructure, service, and technology were emphasized in the past, management and methodology were emphasized recently, and positive perception of smart cities was growing. The study could be used as basic data for the past, present and future of smart cities in Korea at a time when smart city services are being built across the country.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.527-535
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• 2017

As interest increases in health promotion through sports, demand for public sports facilities is steadily growing. However, there is a lack of research on operation and management compared with the supply plan of public sports facility. In this context, the aim of this study is to address problems of management of public sports centers and suggest strategies for vitalizing the facilities through the big-data. The data are collected from web such as news, blog, and cafe for one year in 2015. From the big-data, We can find that the national sports centers and the open gyms showed similar users' behavior but showed different needs. Both facilities have been used as sports and leisure area and have a high percentage of visitors for other purposes such as walking, picnics, etc. However, while the national sports facilities which were used for more specialized programs, the open sports center were used as leisure space.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.1-9
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• 2019

The purposes of the study are to investigate Koreans' perception about Vietnam by analyzing social big data and to seek changing direction in perception. For the purposes, the texts about Vietnam in Naver Blog and Twitter and the number of search and click for Vietnam in Naver were analyzed by Social Metrics of Daum Soft and Datalab of Naver. The study also analyzed the annual change of their interest in Vietnam based on social media. The results showed that Koreans still remember the Vietnam war, have a positive emotion toward Vietnam, and view Vietnam as a country where we can gain mutual benefit by exchange. The findings also indicated that Koreans perceive Vietnam as a favorite tourist spot regardless of age. Meanwhile, children under 12 showed a different pattern of an annual change in perception. It might be a positive sign that Koreans' interest region toward Vietnam would be diversified because children under 12 would be the central axis of cultural contents.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.575-584
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• 2013

Data types used for big-data analysis are very widely, such as news, blog, SNS, papers, patents, sensed data, and etc. Particularly, the utilization of web documents offering reliable data in real time is increasing gradually. And web crawlers that collect web documents automatically have grown in importance because big-data is being used in many different fields and web data are growing exponentially every year. However, existing web crawlers can't collect whole web documents in a web site because existing web crawlers collect web documents with only URLs included in web documents collected in some web sites. Also, existing web crawlers can collect web documents collected by other web crawlers already because information about web documents collected in each web crawler isn't efficiently managed between web crawlers. Therefore, this paper proposed a distributed web crawler. To resolve the problems of existing web crawler, the proposed web crawler collects web documents by RSS of each web site and Google search API. And the web crawler provides fast crawling performance by a client-server model based on RMI and NIO that minimize network traffic. Furthermore, the web crawler extracts core content from a web document by a keyword similarity comparison on tags included in a web documents. Finally, to verify the superiority of our web crawler, we compare our web crawler with existing web crawlers in various experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.3
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• pp.167-173
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• 2017

With increased interest in the fourth industrial revolution represented by artificial intelligence, it has been very active to utilize bigdata and machine learning techniques in almost areas of society. Also, such activities have been realized by development of forecasting systems in various applications. Especially in the movie industry, there have been numerous attempts to predict whether they would be success or not. In the past, most of studies considered only the static factors in the process of prediction, but recently, several efforts are tried to utilize realtime social bigdata produced in SNS. In this paper, we propose the prediction technique utilizing various feedback information such as news articles, blogs and reviews as well as static factors of movies. Additionally, we also experimentally evaluate whether the proposed technique could precisely forecast their revenue targeting on the relatively successful movies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.1-8
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• 2019

Recently, consumer distrust has deepened due to blog posts focusing only on public relations due to 'viral marketing'. In addition, marketing projects such as false writing or exaggerated use of the latter phase are one of the most popular programs in 2016 as they are cheaper and more effective than newspaper and TV ads, and the size of advertising costs is set to be a major means of advertising at '3 trillion 394.1 billion won. From this 'viral marketing,' it has become an Internet environment that needs tools to filter information. The fake review filtering application ADDAVICHI presented in this paper extracts, analyzes, and presents blog keywords, total number of searches, reliability and satisfaction when users search for content such as "event" and "taste restaurant." Reliability shows the number of ad posts on a blog, the total number of posts, and satisfaction shows a clean post with confidence divided into positive and negative posts. Finally, the keyword shows a list of the top three words in the review from a positive post. In this way, it helps users interpret information away from advertising.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.687-698
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• 2022

Purpose: This study was conducted to understand the public awareness of landslide and check dams in 2015-2020 using the big data platform 'Hyean' and to confirm the utilization of this platform in disaster prevention areas. Method: The total amount, number of detection by period by media, and affirmative and negative trends of a search for 'landslide' and 'check dam' in 2015-2020 were analyzed using a keyword search of 'Hyean.' Result: There is significant lack of public awareness of check dam compared to landslide, and the trend is more noticeable in the conspicuous gap of data amount between the news and SNS media. The number and the timing of the search for 'landslide' coincided with the actual occurrence of landslide, while the detection of 'check dam' was less related to it. Relatively affirmative preception for the check dam is inferred, but it was difficult to confirm accurate statistical affirmative and negative trends in the disaster prevention field using 'Hyean.' Conclusion: Unlike the experts who expect positive public awareness of check dam, the statistic results show that the public awareness of the check dam as an effective countermeasure against landslide was extremely low. Active promotion of erosion control projects should be carried out first, and a balanced sample survey should accompany online and periodic field surveys. Since there is a limit to grasping the effective perception in the field of disaster prevention area using 'Hyean', it should be very cautious to establish local/governmental policies using it.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.395-404
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• 2015

This study is exploring objective awareness of literary therapy by consideration of popular perception about literary therapy through analysis of big data. The purpose of this study is the deduction of meaning information through analysis in the viewpoint of big data at online social network service(SNS) about 'literary therapy'. Accordingly, the main way of research became content analysis of keyword linked to literary therapy by utilizing opinion mining method related to text mining. The study mainly grasped 'literary therapy' and analyzed 'bibliotherapy' comparatively. The period of study was from Oct. 10th to Nov. 10th, 2014(during 30 days), and SNS such as blog or twitter became the subject of search. Through the result of study analysis, the conclusion that the spread of literary therapeutic prospect, structural harmony of literary therapeutic field, and the solidity of perceptional axis about literary therapy are needed can be drawn. This study is worthwhile because it can investigate popular awareness about literary therapy and can suggest alternative for invigoration of literary therapy.