• Journal of the Korean Society for Marine Environment & Energy
• /
• v.20 no.2
• /
• pp.117-125
• /
• 2017

SNS Big-data Analysis means to find potential value from big data which has produced by the social media. In this paper, SNS Big-data has been analysed to find Korean concerns by using 24 key words from the marine and fisheries sector. Among 24 key words, seafood, shipping and Dokdo Island are the most mentioned ones. Some key words such as ocean policies and marine security that have less concerns have bess mentioned less. Also, key words that are led by government are mostly mentioned by news media, but key words that are led by private sector and have intimate relationship with people's lives are mostly mentioned by Blogs and Twitters. Therefore, reflecting close national concerns by SNS Big-data Analysis and especially resolving negative factors are the most significant part of the policy establishment. Also, differentiated promotion methods need to be prepared because the frequency of key words mentioned from each type of media are different.

• Journal of Digital Convergence
• /
• v.17 no.7
• /
• pp.35-41
• /
• 2019

This study aims to analyze the cosmedical cosmetics market and the nature of customer through the social big data analysis. More than 80,000 posts were analyzed using R program. After data cleansing, keyword frequency analysis and association analysis were performed to understand customer needs and competitor positioning, formulated several implications for marketing strategy sophistication and implementation. Analysis results show that "prevention" is a new and essential attribute for appealing target customers. The expansion of the product line for the gift market is also suggested. It has been shown that there is a high correlation with products that can be complementary to each other. In addition to the traditional marketing technique, the social big data analysis based on evidence was useful in deriving the characteristics of the customers and the market that had not been identified before. Word2vec algorithm will be beneficial to find additional.

• The Journal of Bigdata
• /
• v.6 no.2
• /
• pp.169-179
• /
• 2021

Consumers' food consumption behavior is likely to be affected not only by structured data such as consumer panel data but also by unstructured data such as mass media and social media. In this study, a deep learning-based consumption prediction model is generated and verified for the fusion data set linking structured data and unstructured data related to food consumption. The results of the study showed that model accuracy was improved when combining structured data and unstructured data. In addition, unstructured data were found to improve model predictability. As a result of using the SHAP technique to identify the importance of variables, it was found that variables related to blog and video data were on the top list and had a positive correlation with the amount of paprika purchased. In addition, according to the experimental results, it was confirmed that the machine learning model showed higher accuracy than the deep learning model and could be an efficient alternative to the existing time series analysis modeling.

• The Journal of the Korea Contents Association
• /
• v.18 no.1
• /
• pp.19-30
• /
• 2018

The main purposes of this study are to analyze the contents and text network of online tourism information. For this purpose, Jeju Island, one of the representative tourist destinations in South Korea is selected as a study site. And this study collects the contents of both JeJu official tourism website and Sina Weibo's personal blogs which is one of the most popular Social Network Systems in China. In addition, this study analyzes this online text information using ROST Content Mining System, one of the Chinese big data mining systems. The results of the content analysis show that the formal website of Jeju includes the nouns related to natural, geographical and physical resources, verbs related to existence of resources, and adjectives related to the beauty, cleanness and convenience of resources mainly. Meanwhile, personal blogs include the nouns of Korean-wave, food, local products, other destinations and shopping, verbs related to activity and feeling in Jeju, and adjectives related to their experiences and feeling mainly. Finally, the results of text network show that there are some strong centrality and network of online tourism information at formal website, but there are weak relationships in personal blogs. The results of this study may be able to contribute to the development of demand-based marketing strategies of tourists destination.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.10a
• /
• pp.742-745
• /
• 2016

빅데이터 시대를 맞이하여 텍스트마이닝과 오피니언마이닝의 활용도가 커지고 있는 시점에서 소셜 네트워크 데이터로부터 유용한 데이터를 추출하는 작업은 매우 중요하다. 이에 본 논문은 블로그 HTML 문서에서 추출한 태그 특징에 로지스틱 회귀 및 앙상블 기법을 적용하여 본문을 포함하는 태그를 분류하는 모델을 구성한 뒤 태그의 깊이 특징을 이용하여 주요 본문을 찾는 방법을 제안한다. 직접 수집한 데이터를 이용한 실험에서 태그 분류 정확도가 0.990, 본문을 찾아낸 문서의 비율이 80.5%로 나왔다.

• Journal of Digital Convergence
• /
• v.13 no.9
• /
• pp.201-208
• /
• 2015

With services such as SNS and facebook, Big Data popularize the use of small size such as micro blogs are increasing. However, the problem of accuracy and computational cost of the search result of big data of a small size is unresolved. In this paper, we propose a subnet selection techniques based probability to improve the browsing speed of the small size of the text information from big data environments, such as micro-blogs. The proposed method is to configure the subnets to give to the attribute information of the data increased the probability data search speed. In addition, the proposed method improves the accessibility of the data by processing a pair of the connection information between the probability of the data constituting the subnet to easily access the distributed data. Experimental results showed the proposed method is 6.8% higher detection rates than CELF algorithm, the average processing time was reduced by 8.2%.

• The Journal of the Korea Contents Association
• /
• v.20 no.2
• /
• pp.561-571
• /
• 2020

Location information service using big data is continuously developing. In terms of navigation, the range of services from map API service to ship navigation information has been expanded, and system application information has been extended to SNS and blog search records for each location. Recently, it is being used as a new industry such as location-based search and advertisement, driverless cars, Internet of Things (IoT) and online to offline (O2O) services. In this study, we propose an information system that enables users to receive information about nearby stores more effectively by using big data when a user moves a specific route. In addition, we have designed this system so that local stores can use this system to effectively promote it at low cost. In particular, we analyzed web-based information in real time to improve the accuracy of information provided to users by complementing the data. Through this system, system users will be able to utilize the information more effectively. Also, from a system perspective, it can be used to create new services by integrating with various web services.

• Journal of The Korean Association of Information Education
• /
• v.25 no.6
• /
• pp.935-945
• /
• 2021

The purpose of this study is to analyze the social perception of Arduino lecture using big data analysis method. For this purpose, data from January 2012 to May 2021 were collected using the Textom website as a keyword searched for 'arduino + lecture' in blogs, cafes, and news channels of NAVER website. The collected data was refined using the Textom website, and text mining analysis and semantic network analysis were performed by opening the Textom website, Ucinet 6, and Netdraw programs. As a result of text mining analysis such as frequency analysis, TF-IDF analysis, and degree centrality it was confirmed that 'education' and 'coding' were the top keywords. As a result of CONCOR analysis for semantic network analysis, four clusters can be identified: 'Arduino-related education', 'Physical computing-related lecture', 'Arduino special lecture', and 'GUI programming'. Through this study, it was possible to confirm various meaningful social perceptions of the general public in relation to Arduino lecture on the Internet. The results of this study will be used as data that provides meaningful implications for instructors preparing for Arduino lectures, researchers studying the subject, and policy makers who establish software education or coding education and related policies.

• Journal of Advanced Navigation Technology
• /
• v.22 no.6
• /
• pp.670-674
• /
• 2018

Social network service (SNS) related data including comments/text, images, videos, blogs, and user experiences contain a wealth of information which can be used to build recommendation systems for various clients' and provide insightful data/results to business analysts. Multimedia data, especially visual data like image and videos are the richest source of SNS data which can reflect particular region, and cultures values/interests, form a gigantic portion of the overall data. Mining such huge amounts of data for extracting actionable intelligence require efficient and smart data analysis methods. The purpose of this paper is to focus on this particular modality for devising ways to model, index, and retrieve data as and when desired.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.4
• /
• pp.375-380
• /
• 2022

The purpose of this study is to find out consumers' perception and meaning of convenience store convenience food by using big data. For this study, NNAVER and Daum analyzed news, intellectuals, blogs, cafes, intellectuals(tips), and web documents, and used 'convenience store convenience food' as keywords for data search. The data analysis period was selected as 3 years from January 1, 2019 to December 31, 2021. For data collection and analysis, frequency and matrix data were extracted using TEXTOM, and network analysis and visualization analysis were conducted using the NetDraw function of the UCINET 6 program. As a result, convenience store convenience foods were clustered into health, diversity, convenience, and economy according to consumers' selection attributes. It is expected to be the basis for the development of a new convenience menu that pursues convenience and convenience based on consumers' meaning of convenience store convenience foods such as appropriate prices, discount coupons, and events.