• International Journal of Contents
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• 제10권4호
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• pp.18-22
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• 2014

Social network games (SNGs), a rapidly growing online game genre, are built and played on social network sites. SNGs provide an online world for enjoying leisure time and interpersonal communication, and an increasing numbers of college students are involved in such game-playing as a leisure time activity. Despite the popularity, relatively few studies have been conducted to investigate the nature of game players, especially the meaning of such leisure time behavior by college students. This paper's aim was to explore a subjective meaning structure of online social network game play. The means-end chain model was used to link attributes of SNGs to the underlying values of game playing as a leisure activity. The results revealed two emerging end-values: the need for bridging and a sense of belonging. This study sheds light on the meaning of college students' leisure activities when playing social network games.

• 한국멀티미디어학회논문지
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• 제18권4호
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• pp.524-532
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• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

• International Journal of Contents
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• 제13권3호
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• pp.43-48
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• 2017

As a computational approach to literary criticism, the method of character network analysis has attracted attention. The character network is composed of nodes as characters and links as relationship between characters, and has been used to analyze literary works systematically. However, there were limitations in that relationships between characters were so superficial that they could not reflect intimate relationships and quantitative data from the network were not interpreted in depth regarding meaning of literary works. In this study, we propose an improved method of character network analysis through a case study on the play . First, we segmented the character network into a dialogue network focused on speaker-to-listener relationship and an opinion network focused on subject-to-object relationship. We analyzed these networks in various ways and discussed how analysis results could reflect structure and meaning of the work. Through these studies, we strived to find a way of organic and meaningful connection between literary criticism in humanities and network analysis in computer science.

• 한국어학
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• 제78권
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• pp.31-54
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• 2018

'맞다(right)', which is inversely related to 'teullida', has a synonymic relationship with '같다(same)' depending on the sense. Naturally, the '같다' is usually inversely related to '다르다(be different)' as symmetry verb. The meaning of '다르다' is 'teullida' and there is a close meaning relationship network in the network of words. In other words, the process acting as a driver of the expansion of meanings based on the antonym-relation of (1)'틀리다${\leftrightarrow}$맞다', and the s?ynonym-relation of (2)'맞다 = 같다' forms a network, and the relation between them and the opposite semantics is (3)'같다=맞다${\leftrightarrow}$다르다'. And many of today's speakers speak (4)'teullida' of [difference]. Therefore, after the application of the synonymic analogy, eventually, the antonymic analogy is formed, and the word formed is 'teullida' of [difference]. This, of course, forms another type of enlargement of the meaning.

• 한국경제지리학회지
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• 제16권1호
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• pp.50-70
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• 2013

창조경제 새로운 패러다임의 변화는 사회적 관계의 변화에 영향을 끼치고 있으며, 소셜 네트워크 서비스 등의 발전에 따라 나타나는 새로운 관계의 공간적 특성에도 영향을 끼치고 있다. 본 논문에서는 이러한 변화에 영향을 미치는 동력을 '소셜 네트워크 리소스(social network resource)'로 명명하고, 그에 따라 나타나는 제반 특징과 경제지리학적 관점에서의 공간적 특성을 설명하고자 하였다. '소셜 네트워크 리소스'는 개방성과 공유, 참여, 협력의 특징을 보여주는 동시에, 공간적으로는 로컬과 글로벌의 특징을 모두 아우르는 소위 '트랜스 로컬리티'의 특성을 보이고 있는데, 서울시의 사회적 지식공유 웹 플랫폼인 '위키서울닷컴'의 사례를 통해 그러한 특성을 확인할 수 있었다. 특히, 물리적자원, 인적자원, 정보자원의 특성과 함께 관계자원으로서의 특징이 모두 나타나고 있으며, 이러한 특징에 공간이 투영되어, 사회적 관계가 공간에 표출되는 질적 공간의 특성 또한 나타남을 확인할 수 있었다.

• 한국IT서비스학회지
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• 제17권2호
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• 한국컴퓨터정보학회논문지
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• 제22권4호
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• pp.41-49
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• 2017

Word sense disambiguation(WSD) that determines the exact meaning of homonym which can be used in different meanings even in one form is very important to understand the semantical meaning of text document. Many recent researches on WSD have widely used NNLM(Neural Network Language Model) in which neural network is used to represent a document into vectors and to analyze its semantics. Among the previous WSD researches using NNLM, RNN(Recurrent Neural Network) model has better performance than other models because RNN model can reflect the occurrence order of words in addition to the word appearance information in a document. However, since RNN model uses only the forward order of word occurrences in a document, it is not able to reflect natural language's characteristics that later words can affect the meanings of the preceding words. In this paper, we propose a WSD scheme using Bidirectional RNN that can reflect not only the forward order but also the backward order of word occurrences in a document. From the experiments, the accuracy of the proposed model is higher than that of previous method using RNN. Hence, it is confirmed that bidirectional order information of word occurrences is useful for WSD in Korean language.

• 디지털콘텐츠학회 논문지
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• 제15권5호
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• pp.621-630
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• 2014

본 연구는 G. 레이코프와 M. 존슨이 제시한 '개념적 은유'를 적용해 소셜 네트워크 서비스의 은유적 특성을 분석하는 것을 목적으로 한다. 소셜 네트워크 서비스는 일방향적 소통만 가능했던 기존 매체와는 달리, 사용자가 참여적으로 자신의 일상을 표현하고 타인의 서사를 해석하는 과정을 통해서 그 텍스트를 확장한다. 기존 매체에서 수동적이던 독자가, SNS에서 적극적인 발화자로 전환될 수 있었던 가장 큰 이유는 SNS에 내재된 은유적 속성 때문이다. 이에 본 연구에서는 SNS 사용자 텍스트의 생성 및 해석 과정을 '근원영역'과 '목표영역'을 중심으로 고찰했다. 이후 은유적 속성을 통해 나타난 사용자 텍스트의 생성구조를 폴 리쾨르의 삼중 미메시스를 통해 증명했다. SNS를 환유로 전제했던 기존의 연구 관점과 달리, 본 연구는 SNS의 은유적 특성 및 의미를 제시했다는 점에서 그 의의가 있다.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.204-210
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• 2022

From the 1970s to the present, the number of new infectious diseases such as SARS, Ebola virus, and MERS has steadily increased. The new infectious disease, COVID-19, which began in Wuhan, Hubei Province, China, has pushed the world into a pandemic era. As a result, Countries imposed restrictions on entry to foreign countries due to concerns over the spread of COVID-19, which led to a decrease in the movement of tourists. Due to the restriction of travel, keywords such as "Corona blue" have soared and depression has increased. Therefore, this study aims to analyze the stress meaning network of the COVID-19 era to derive keywords and come up with a plan for a travel-related platform of the Post-COVID 19 era. This study conducted analysis of travel and stress caused by COVID-19 using TEXTOM, a big data analysis tool, and conducted semantic network analysis using UCINET6. We also conducted a CONCOR analysis to classify keywords for clustering of words with similarities. However, since we have collected travel and stress-oriented data from the start to the present, we need to increase the number of analysis data and analyze more data in the future.

• 디지털산업정보학회논문지
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• 제16권4호
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• pp.75-93
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• 2020

The purpose of this study is to extract keywords for 'startup' and 'entrepreneurship' from Naver news articles in Korea since 1990 and Google news articles in foreign countries, and to understand the changes in the meaning of entrepreneurship and entrepreneurship in each era It is aimed at doing. In summary, first, in terms of the frequency of keywords, venture sprouting is a sample of the entrepreneurial spirit of the government-led and entrepreneurs' chairman, and various technology investments and investments in corporate establishment have been made. It can be seen that training for the development of items and items was carried out, and in the case of the venture re-emergence period, it can be seen that the youth-oriented entrepreneurship and innovation through the development of various educational programs were emphasized. Second, in the result of vocabulary network analysis, the network connection and centrality of keywords in the leap period tended to be stronger than in the germination period, but the re-leap period tended to return to the level of germination. Third, in topic analysis, it can be seen that Naver keyword topics are mostly business-related content related to support, policy, and education, whereas topics through Google News consist of major keywords that are more specifically applicable to practical work.