• Journal of the Korea Convergence Society
• /
• v.11 no.6
• /
• pp.145-151
• /
• 2020

'Hansik', or Korean cuisine is one of Korea national brands. To understand the domestic consumer awareness of Korean cuisine, data was gathered under the keyword search, 'Hansik.' Textom 3.5 was used to gather data from blogs, news media found on Naver from November 1, 2018, to October 31, 2019. The results from frequency and TF-IDF analysis indicate that the 'buffet' had the largest proportion in terms of consumer awareness to Hansik. Also, broadcasting contents starring star chefs had a great influence. The Hansik awareness did not remain in the domains of its traditionality, but also branched into extents into areas such as fusional and gourmet cuisine. UCINET6 and NetDraw were used to conduct CONCOR analysis. Four cluster formations have been found; various food cultural cluster, high-end restaurant cluster referring to aired restaurants on media, Hansik brand cluster, and Hansik buffet cluster. This study proposes presenting a various menu of Hansik which use a multiple number of ingredients. Also, a promotion that introduces fine Hansik and a development of marketing views and media contents about the convenient HMRs make the associated imagery of Hansik to be strengthen.

• Journal of Intelligence and Information Systems
• /
• v.15 no.1
• /
• pp.87-102
• /
• 2009

With the growth of Web 2.0, lots of services allow yours to post their personal information and useful knowledges on networked information spaces such as blogs and online communities etc. As the services are generalized, recent researches related to social network have gained momentum. However, most social network services do not support machine-processable semantic knowledge, so that the information cannot be shared and reused between different domains. Moreover, as explicit definitions of relationships between individual social entities do not be described, it is difficult to analyze social network for inferring unknown semantic relationships. To overcome these limitations, in this paper, we propose a social network analysis system with personal photographic data up-loaded by virtual community users. By using ontology, an informative connectivity between a face entity extracted from photo data and a person entity which already have social relationships was defined clearly and semantic social links were inferred with domain rules. Then the inferred links were provided to yours as a visualized graph. Based on the graph, more efficient social network analysis was achieved in online community.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.12
• /
• pp.2878-2884
• /
• 2015

Twitter, one of online social network services, is one of the most popular micro-blogs, which generates a large number of automated programs, known as tweet bots because of the open structure of Twitter. While these tweet bots are categorized to legitimate bots and malicious bots, it is important to detect tweet bots since malicious bots spread spam and malicious contents to human users. In the conventional work, temporal information was utilized for the classficiation of human and bot. In this paper, by utilizing geo-tagged tweets that provide high-precision location information of users, we first identify both Twitter users' exact location. Then, we propose a new tweet bot detection algorithm by using both an entropy based on geographic variable of each user and device information of each user. As a main result, the proposed algorithm shows superior bot detection and false alarm probabilities over the conventional result which only uses temporal information.

• Science of Emotion and Sensibility
• /
• v.12 no.4
• /
• pp.433-442
• /
• 2009

People are tending to buy products through the Internet rather than purchasing them from the store. Some of the consumers give their feedback on line such as reviews, replies, comments, and blogs after they purchased the products. People are also likely to get some information through the Internet. Therefore, companies and public institutes have been facing this situation where they need to collect and analyze reviews or public opinions for them because many consumers are interested in other's opinions when they are about to make a purchase. However, most of the people's reviews on web site are too numerous, short and redundant. Under these circumstances, the emotion scanning system of text documents on the web is rising to the surface. Extracting writer's opinions or subjective ideas from text exists labeled words like GI(General Inquirer) and LKB(Lexical Knowledge base of near synonym difference) in English, however Korean language is not provided yet. In this paper, we labeled positive, negative, and neutral attribute at 4 POS(part of speech) which are noun, adjective, verb, and adverb in Korean dictionary. We extract construction patterns of emotional words and relationships among words in sentences from a large training set, and learned them. Based on this knowledge, comments and reviews regarding products are classified into two classes polarities with positive and negative using SO-PMI, which found the optimal condition from a combination of 4 POS. Lastly, in the design of the system, a flexible user interface is designed to add or edit the emotional words, the construction patterns related to emotions, and relationships among the words.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.6
• /
• pp.662-672
• /
• 2017

Child sexual crimes have increased, but there has been no effective plan to combat this. Films reporting problems, amplify the attentions and propose countermeasures, which leads to changes. The current study examined the audiences' reactions to child sexual crime faction films using text-mining. The analysis of Naver's 2,727 blogs showed realistic words while 3,000 review comments' analysis demonstrated emotional responses. The positive and negative emotional category and degree were also different. In , the higher degree of negative emotions, such as 'angry' and 'unpleasant' appeared frequently. In , only negative emotional worlds were used. On the other hand, 'sad' was the highest ranked word, and the negative level was weak. In , 'good' a positive emotional word solely emerged. The audiences perceived the accidents objectively before release while they expressed their emotions and feelings after watching the movies. caused explosive anger and organized the participating citizens for changes. This movie provided an opportunity to enforce a legislative bill intensifying heavy punishments. The present study is significant in scrutinizing the audiences' diverse emotional reactions and discusses the future direction of society prosecution movies. Based on the text analysis of the audiences' linguistic expressions, a future study will be needed to hierarchically classify the diverse emotional expressions.

• The Journal of the Korea Contents Association
• /
• v.16 no.11
• /
• pp.253-263
• /
• 2016

We analyzed the social issue 'image of the elderly' using Big Data and Social Network Analysis. First, we analyzed the words extracted by the text mining technique by inputting the keyword 'elderly'. As a result of analysis, the image of the elderly viewed through media such as cafes, blogs, etc. Representing the trend of the public was using the word 'Senior' the most. The image of the elderly is expressed using the word having the highest frequency in the top 10, "The elderly are 'Senior' people who are respected by society, they are organized to earn money, to earn their qualifications, to health, and to 'Seniors' who desire to work healthy up to 100 years old". The purpose of this study is to differentiate from the existing analysis method by analyzing the macro-level image of the elderly including the social discourse by collecting vast amount of data and analyzing it with the social networking technique. When the image of the elderly that the public perceives is positively expressed as 'Senior', it can be said that the direction of the current elderly policy is evaluated as a desirable direction. On the other hand, it was able to feel the 'desire' of the public who wanted to be evaluated. Therefore, the policy direction of the elderly to be applied in the future should be the policy that enables the elderly to be perceived as 'Necessary existence' in society by taking on social roles. In addition, we proposed to implement the policy of the elderly that reflects priorities such as job creation, welfare, and alienation that can activity and maintain health.

• Journal of Digital Convergence
• /
• v.18 no.11
• /
• pp.571-579
• /
• 2020

The purpose of the study was to explore modern people's emotion regarding sex commercialization related to the abolition of grid girl. To collect data, based on 'reply journalism', this study collected 15 blogs, 10 online cafe contents, 1 youtube video clip, and 364 replies associated with the three online contents. To analyze the data, interpretive text analysis was utilized and the following results were obtained. As results, the analysis on the replies shows that the most strong emotion of the modern people regarding the abolition of grid girl is anti-feminism that includes hatred toward feminists and even females, criticism on feminism, and notion of 'women's enemy is women themselves'. In addition, sympathy toward racing models who lost their jobs, requirement of same abolition to the people with similar occupations, spatial separation between men and women, and consent on the abolition of racing models were found. Unlike the feminists' emotion regarding sex commercialization and racing models, modern people's emotion was different from them. Rather, ordinary people have doubted and even criticized on the rationales of feminism. Unlike feminists' notion about sex commercialization of racing models, these results imply that social image of racing models has changed and wish their position is respected as an ordinary occupation, without issues of sex commercialization.

• Korean Journal of Health Education and Promotion
• /
• v.30 no.3
• /
• pp.59-73
• /
• 2013

Objectives: The study is aimed at examining the individual reasons and regional/environmental factors of online search on suicide using social big data to predict practical behaviors related to suicide and to develop an online suicide prevention system on the governmental level. Methods: The study was conducted using suicide-related social big data collected from online news sites, blogs, caf$\acute{e}$s, social network services and message boards between January 1 and December 31, 2011 (321,506 buzzes from users assumed as adults and 67,742 buzzes from those assumed as teenagers). Technical analysis and development of the suicide search prediction model were done using SPSS 20.0, and the structural model, nd multi-group analysis was made using AMOS 20.0. Also, HLM 7.0 was applied for the multilevel model analysis of the determinants of search on suicide by teenagers. Results: A summary of the results of multivariate analysis is as follows. First, search on suicide by adults appeared to increase on days when there were higher number of suicide incidents, higher number of search on drinking, higher divorce rate, lower birth rate and higher average humidity. Second, search on suicide by teenagers rose on days when there were higher number of teenage suicide incidents, higher number of search on stress or drinking and less fine dust particles. Third, the comparison of the results of the structural equation model analysis of search on suicide by adults and teenagers showed that teenagers were more likely to proceed from search on stress to search on sports, drinking and suicide, while adults significantly tended to move from search on drinking to search on suicide. Fourth, the result of the multilevel model analysis of determinants of search on suicide by teenagers showed that monthly teenagers suicide rate and average humidity had positive effect on the amount of search on suicide. Conclusions: The study shows that both adults and teenagers are influenced by various reasons to experience stress and search on suicide on the Internet. Therefore, we need to develop diverse school-level programs that can help relieve teenagers of stress and workplace-level programs to get rid of the work-related stress of adults.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.5
• /
• pp.1047-1059
• /
• 2015

This study will predict risk factors associated with cigarettes in Korea by analyzing the social big data collected from the internet such as blogs, cafes, and SNSes in Korea, using data mining techniques. The key analysis results are as follows. First, when "raising cigarette price"is mentioned online, the negative group (i.e., the proportion of people holding negative views about smoking) increased from 58.6% to 74.8%, and when "lung cancer" is mentioned, it increased to 73.1%. Second, with regard to cigarettes in general, the positive group (i.e., the proportion of people holding positive views about smoking) decreased by 5.6% after the raising of cigarette prices, while the negative group increased by 6.1%. Third, when policies related to "FCTC, raising cigarette price, non-smoking laws, smoking regulations, non-smoking ads, and nonsmoking business" are more frequently mentioned online, the positive group tended to decrease. Finally, when "non-smoking drugs, non-smoking patches, and non-smoking gums" are more frequently mentioned online, the positive group tended to decrease. However, when "electronic cigarettes and supplements" are more frequently mentioned online, the positive group increased.

• The Journal of the Korea Contents Association
• /
• v.11 no.10
• /
• pp.19-29
• /
• 2011

A Blog provides commentary, news, or content on a particular subject. The important part of many blogs is interactive format. Sometimes, there is a heated debate on a topic and any article becomes a political or sociological issue. In this paper, we proposed a method to predict the popularity of an article in advance. First, we used hit count as a factor to predict the popularity of an article. We defined the saturation point and derived a model to predict the hit count of the saturation point by a correlation coefficient of the early hit count and hit count of the saturation point. Finally, we predicted the virtual temperature of an article using 4 types(explosive, hot, warm, cold). We can predict the virtual temperature of Internet discussion articles using the hit count of the saturation point with more than 70% accuracy, exploiting only the first 30 minutes' hit count. In the hot, warm, and cold categories, we can predict more than 86% accuracy from 30 minutes' hit count and more than 90% accuracy from 70 minutes' hit count.