• 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.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.55-63
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• 2021

Since social big data often includes new words or proper nouns, statistical morphological analysis methods have been widely used to process them properly which are based on the frequency of occurrence of each word. However, these methods do not properly recognize compound nouns, and thus have a problem in that the accuracy of keyword extraction is lowered. This paper presents a method to extract compound nouns in keyword analysis of social big data. The proposed method creates a candidate group of compound nouns by combining the words obtained through the morphological analysis step, and extracts compound nouns by examining their frequency of appearance in a given review. Two algorithms have been proposed according to the method of constructing the candidate group, and the performance of each algorithm is expressed and compared with formulas. The comparison result is verified through experiments on real data collected online, where the results also show that the proposed method is suitable for real-time processing.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.99-108
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• 2012

Many methods had been developed to improve the accuracy of extracting information from a vast amount of data. This paper combined a number of natural language processing methods such as NER (named entity recognition), sentence extraction, and part of speech tagging to carry out text analysis. The data source is comprised of texts obtained from the web using a domain-specific data extraction agent. A framework for the extraction of information from unstructured data was developed using the aforementioned natural language processing methods. We simulated the performance of our work in the extraction and analysis of texts for the detection of organizational structures. Simulation shows that our study outperformed other NER classifiers such as MUC and CoNLL on information extraction.

• The Journal of Information Systems
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• v.28 no.4
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• pp.293-311
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• 2019

Purpose This paper is to make a meta-analysis of the relationship between the social media use and learning performance as well as its potential moderating variables to clarify the differences in research conclusions in existing literatures, and refine the situational and method factors that affect the relationship between them. Methodology Meta-analysis used in this study can combine the quantitative data from different empirical studies, focus on the same research problem, and finally reach a research conclusion. Findings The results show that social media use and learning performance have a moderating positive correlation. The moderating effect test of usage scenarios shows that social media types, usage groups, application platforms and discipline fields have moderating effects on the relationship between social media use and learning performance. The moderating effect test of the research method found that measurement models, data attributes and learning performance indicators also had moderating effects on the relationship between social media use and learning performance.

• Journal of Service Research and Studies
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• v.5 no.1
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• pp.79-89
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• 2015

Recently, with the introduction of social network services, studies that try to make use of them for the various purposes have been actively investigated. In order to proceed with the research that takes advantage of social network services, it is necessary to review the relevant literature and to identify trends in researches. However, the researches of social network are massive amount, so to review the huge amount of relevant research literature is a very difficult task. Therefore, in this study, we analyze systematically the tendency of research related to social network service focusing on Twitter. Especially, we use the SLR (Systematic Literature Review) technique for systematic literature survey and analysis. For the literature survey, we select korean literature resource sites and 243 studies of literature that are surveyed. Studies and analyzes on Twitter in a variety of research studies were also using Twitter data that way beyond the simple question directly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.127-129
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• 2015

A meta-analysis is a statistical integration method that delivers an opportunity to overview the entire result of integrating and analyzing many quantitative research results. This study will find meaningful independent variables for criterion variables that affect influencing on purchase intention in social network service, on the basis of the results of a meta-analysis. We reviewed a total of 29 studies related on purchase intention in social network service published in Korean journals between 2000 and 2015, where a cause and effect relationship is established between variables that are specified in the conceptual model of this study. Thus, we present the theoretical and practical implications of these results and discuss the differences between these results through a comparative analysis with previous studies.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.2
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• pp.89-96
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• 2015

Location-based social media data have high potential to be used in various area such as big data, location based services and so on. In this study, we applied a series of analysis methodology to figure out how the important keywords in location-based social media are spatially distributed by analyzing text information. For this purpose, we collected tweet data with geo-tag in Gangnam district and its environs in Seoul for a month of August 2013. From this tweet data, principle keywords are extracted. Among these, keywords of three categories such as food, entertainment and work and study are selected and classified by category. The spatial clustering is conducted to the tweet data which contains keywords in each category. Clusters of each category are compared with buildings and benchmark POIs in the same position. As a result of comparison, clusters of food category showed high consistency with commercial areas of large scale. Clusters of entertainment category corresponded with theaters and sports complex. Clusters of work and study showed high consistency with areas where private institutes and office buildings are concentrated.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.571-578
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• 2010

Recently, many social networking services using mobile devices are spread. Also, many studies based on location and time are increasing. However, existing studies have been difficult to resolve queries by place, time, and events. In the paper, we propose time and space modeling method for social services. We propose Human Activity Graph and Quad Relation Factors through time, place, event, and social activity of users, and we design the database scheme for data collect and analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1294-1297
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• 2011

최근, Flickr, YouTube 와 같은 사용자 참여형 미디어 공유 및 검색 사이트가 폭발적으로 증가하면서, 이를 멀티미디어 정보 검색 서비스에 효과적으로 활용하기 위한 다양한 연구들이 시도되고 있다. 특히, 이미지에 할당되어 있는 태그를 이용하여 이미지를 효과적으로 검색하기 위한 연구가 활발히 진행 중이다. 그러나 사용자들에 의해 제공되는 소셜 이미지들은 매우 다양한 범위와 주제를 가지고 있기 때문에, 소셜 이미지들의 분류 및 태그 할당을 위한 트레이닝 집합의 획득이 쉽지 않다는 한계점을 가지고 있다. 본 논문에서는 데이터 군집화를 위한 클러스터링 알고리즘들 중 K-Means, K-Medoids, Affinity Propagation 을 활용하여 소셜 이미지 집합으로부터 트레이닝 집합을 획득하기 위한 방법들을 살펴 본다. 또한, 각 알고리즘으로부터 획득한 트레이닝 집합을 이용하여 소셜 이미지를 분류한 결과를 비교 분석한다.

• Knowledge Management Research
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• v.21 no.3
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• pp.141-160
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• 2020

This study provides a new perspective on the effect of social media marketing activities (SMMA) on purchase intention in Korean cosmetic firms. The increasing use of social media has changed how firms engage their brand with consumers. This phenomenon triggered a need for this research to examine further the influence of SMMA on social brand engagement (SBE), brand equity (BE), and purchase intention (PI). The purpose of this paper is to investigate the effect of SMMA on purchase intention in Korean cosmetic firms with brand equity and social brand engagement. The factors of SMMA were identified based on previous literature reviews that have an impact on social media marketing activity. To empirically test the effects of SMMA, this study conducted a questionnaire survey on 219 social media users for data analysis out of the initial 332 survey data. The results reveal that all five SMMA elements are positively related to BE, SBE, and PI. The study enables cosmetic brands to forecast the future purchasing behavior of their customers more accurately and brings clarity to manage their assets and marketing activities as well.