• Journal of the Korea Convergence Society
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• v.9 no.8
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• pp.25-32
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• 2018

Recently, the size and type of disasters in Korea has been diversified. However, Korea has not been able to build various information support systems to predict these disasters.Many other organizations also provide relevant information. This information is mainly provided on the Web, but most of it is not real time information. In this study, we have paid attention to support information using big data to provide better quality real - time information together with information provided by institutions. Big data has a large amount of information with real-time property, and it can make customized service using it. Among them, SNS such as Twitter and Facebook can be used as a new information collection medium in case of disaster. However, it is very difficult to retrieve necessary information from too much information, and it is difficult to collect intuitive information. For this purpose, this study develops an information support system using Twitter. The system retrieves information using the Twitter hashtag. Also, information mapping is performed on the map so that intuitive information can be grasped. For system evaluation, information extraction, degree of mapping, and recommendation speed are evaluated.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.8
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• pp.301-308
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• 2015

SNS is a web-based online platform service supporting the formation of relations between users. SNS users have usually used a desktop or laptop for this purpose so far. However, the number of SNS users is greatly increasing and their access to the web is improving with the spread of smart phones. They share their daily lives with other users through SNSs. We can detect events if we analyze the contents that are left by SNS users, where the individual acts as a sensor. Such analyses have already been attempted by many researchers. In particular, Twitter is used in related spheres in various ways, because it has structural characteristics suitable for detecting events. However, there is a limitation concerning the detection of events and their locations. Thus, we developed a system that can detect the location immediately based on the district mentioned in Twitter. We tested whether the system can function in real time and evaluated its ability to detect events that occurred in reality. We also tried to improve its detection efficiency by removing noise.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.263-270
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• 2014

This paper suggests a method for predicting a box-office success of the film. Lately, as the growth of the film industry, a variety of studies for the prediction of market demand is being performed. The product life cycle of film is relatively short cultural goods. Therefore, in order to produce stable profits, marketing costs before opening as well as the number of screen after opening need a plan. To fulfill this plan, the demand for the product and the calculation of economic profit scale should be preceded. The cases of existing researches, as a variable for predicting, primarily use the factors of competition of the market or the properties of the film. However, the proportion of the potential audiences who purchase the goods is relatively insufficient. Therefore, in this paper, in order to consider people's perception of a movie, Twitter was utilized as one of the survey samples. The existing variables and the information extracted from Twitter are defined as off-line and on-line element, and applied those two elements in machine learning by combining. Through the experiment, the proposed predictive techniques are validated, and the results of the experiment predicted the chance of successful film with about 95% of accuracy.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.177-184
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• 2017

The abstract should concisely state what was done, how it was done, principal results, and their significance. It should be less than 300 words for all forms of publication. Recently social disasters have been occurring frequently in the increasing complicated social structure, and the scale of damage has also become larger. Accordingly, there is a need for a way to prevent further damage by rapidly responding to social disasters. Twitter is attracting attention as a countermeasure against disasters because of immediacy and expandability. Especially, collecting public opinion on Twitter can be used as a useful tool to prevent disasters by quickly responding. This study proposes a collecting method of Twitter public opinion through keyword analysis, issue topic tweet detection, and time trend analysis. Furthermore we also show the feasibility by selecting the case of humidifier disinfectant which is a social issue recently.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.345-350
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• 2016

This research suggests the keyword filtering about disaster and the method of detecting area in real-time event detecting system by analyzing contents of twitter. The diffusion of smart-mobile has lead to a fast spread of SNS and nowadays, various researches based on studying SNS are being processed. Among SNS, the twitter has a characteristic of fast diffusion since it is written in 140 words of short paragraph. Therefore, the tweets that are written by twitter users are able to perform a role of sensor. By using these features the research has been constructed which detects the events that have been occurred. However, people became reluctant to open their information of location because it is reported that private information leakage are increasing. Also, problems associated with accuracy are occurred in process of analyzing the tweet contents that do not follow the spelling rule. Therefore, additional designing keyword filtering and the method of area detection on detecting real-time event process were required in order to develop the accuracy. This research suggests the method of keyword filtering about disaster and two methods of detecting area. One is the method of removing area noise which removes the noise that occurred in the local name words. And the other one is the method of determinating the area which confirms local name words by using landmarks. By applying the method of keyword filtering about disaster and two methods of detecting area, the accuracy has improved. It has improved 49% to 78% by using the method of removing area noise and the other accuracy has improved 49% to 89% by using the method of determinating the area.

• Journal of the Korean Society for information Management
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• v.38 no.4
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• pp.113-128
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• 2021

In this study, we aimed to understand the public opinion on COVID-19 vaccine. To achieve the goal, we analyzed COVID-19 vaccine-related Twitter posts. 45,413 tweets posted from March 16, 2020 to March 15, 2021 including COVID-19 vaccine names as keywords were collected. The 12 vaccine names used for data collection included 'Pfizer', 'AstraZeneca', 'Modena', 'Jansen', 'NovaVax', 'Sinopharm', 'SinoVac', 'Sputnik V', 'Bharat', 'KhanSino', 'Chumakov', and 'VECTOR' in the order of the number of collected posts. The collected posts were analyzed manually and automatedly through keyword analysis, sentiment analysis, and topic modeling to understand the opinions for the investigated vaccines. According to the results, there were generally more negative posts about vaccines than positive posts. Anxiety about the aftereffects of vaccination and distrust in the efficacy of vaccines were identified as major negative factors for vaccines. On the contrary, the anticipation for the suppression of the spread of coronavirus following vaccination was identified as a positive social factor for vaccines. Different from previous studies that investigated opinions about COVID-19 vaccines through mass media data such as news articles, this study explores opinions of social media users using keyword analysis, sentiment analysis, and topic modeling. In addition, the results of this study can be used by governmental institutions for making policies to promote vaccination reflecting the social atmosphere.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.387-388
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• 2015

본 연구에서는 스마트미디어를 사용하여 캘리그래피 활용을 나타낸다. 스마트기기를 이용한 SNS로 트위터, 페이스북 등 다양한 수단을 이용하여 캘리그래피를 CI, BI, 영화 포스터, 책 표지타이틀, 전시, 마케팅 전략 등 기업의 수익 모델과 개인 작품 활동들을 공유함으로써 다양한 정보제공과 영상으로 그 효과는 매우 크다 따라서 이러한 스마트기기를 활용한 캘리그래피는 앞으로도 더 많은 정보 제공과 수익 모델을 창출하며 그 영향력은 매우 증대될 것으로 사료한다.

• Journal of Digital Convergence
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• v.14 no.3
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• pp.165-170
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• 2016

The purpose of this study was to explore the differences in sentiment on social networking sites among six languages (English, German, Russian, Spanish, Turkish and Dutch). A total of 204 million tweets were collected using Streaming API. Subjective/objective ratio, sentiment strength, positive/negative ratio, number of retweets and boundary impermeability were analyzed with SentiStrength to estimate the trends of emotional expression via Twitter. The results showed that subjective/objective ratio and the positive/negative ratio of tweets were significantly different by languages (p<0.001). And, there were significant effects of language on sentiment strength, boundary impermeability and the number of retweets (p<0.001). The results also indicate that the cross-cultural, language differences should be taken into account in sentiment analysis on SNS.

• International Commerce and Information Review
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• v.13 no.4
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• pp.3-21
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• 2011

In this study, several experiments were designed to test the effectiveness of social media in export marketing. In particular, the experiments were made using Twitter and Facebook. The results showed that users' interest were able to increase the effects combined with B2B and B2C marketing events. The B2C marketing events could be made by personal target Event, Poll event, guest comments and social commerce. The B2B marketing was performed using Page Manager, Affiliate page, building and affiliate marketing group. Special features of Facebook such as social plug-in, Twitter integration, and Photo Tagging were found effective. A couple of implications were found in this study. First, the link between social media channel system was key success factor in effective export marketing. Second, the corporate marketing mix and social media consistent with the marketing mix strategy, communication between the managers and the managers' competencies were obtained for the key success factors.

• Journal of Creative Information Culture
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• v.5 no.3
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• pp.215-223
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• 2019

The wave of the information age gradually accelerates, and fusion analysis solutions that can utilize these knowledge data according to accumulation of various forms of big data such as large capacity texts, sounds, movies and the like are increasing, Reduction in the cost of storing data accordingly, development of social network service (SNS), etc. resulted in quantitative qualitative expansion of data. Such a situation makes possible utilization of data which was not trying to be existing, and the potential value and influence of the data are increasing. Research is being actively made to present future-oriented education systems by applying these fusion analysis systems to the improvement of the educational system. In this research, we conducted a big data analysis on Twitter, analyzed the natural language of the data and frequency analysis of the word, quantitative measure of how domestic windows education problems and outcomes were done in it as a solution.