• Journal of Digital Convergence
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• v.19 no.7
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• pp.79-85
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• 2021

After COVID-19, the number of individuals participating in delivery platforms has increased. They are using the participation of the delivery platform as a means of creating a new source of income as well as a means of sports and hobbies. This phenomenon is related to a social phenomenon called 'N-jober'. However, there are still few studies examining this phenomenon. Therefore, this study intends to examine the phenomenon of individual participation in delivery platforms and their issues. Text mining was performed on news data from January 2019, when COVID-19 started. As a result, social issues related to the increase in individual participation in delivery platforms were derived into 5 topics(Introduction to the Phenomenon, Characteristics of Participants, Participant's Income and Fees, Characteristics as a Job, Concern about Potential Risks). This study has significance in that it expanded the perspective of academic discussion on delivery platform business to individual participants.

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

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.235-238
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• 2006

최근 ATSC, DVB, DMB 및 인터넷 등에서 경제적으로 고속 정보전송이 가능해 짐에 따라 교통정보 서비스 프로토콜인 TPEG이 XML형태로 개발되어 시험 서비스 되고 있다. 기존의 RTM, PTI 응용프로토콜에 대한 XML 버전을 참고하여, 현재 시험 서비스 중인 News, POI 응용 프로토콜에 대한 XML 버전을 제안한다. 교통정보제공자로부터 공급된 원 뉴스정보 및 위치기반 정보를 XML 파일로 인코딩하여 송신하고, 수신측에서 xsl 파일을 제작하여 수신된 XML 파일을 디코딩한 후, 교통정보를 출력하여 보임으로서 그 기능을 검증한다. 테이블 및 속성들에 대한 언어독립적인 ENTITY 와 DTD를 설계하여, 원 정보로부터 제작된 XML 파일에 대한 적합성을 검증할 수 있도록 하였고, 수신된 XML파일을 단말형태에 적합하게 표현하기 위해 xsl 파일을 제작하여 수신된 파일의 표현성 및 확장성이 용이하도록 설계하였다.

• The Journal of the Korea Contents Association
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• v.13 no.7
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• pp.150-157
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• 2013

In this media convergence era when the 'hybrid' genres increase, it has been important to explore distinguishing genre-specific effects of contents use. This study revealed that news, current affairs/talk, and documentary genres respectively produces opposite directions of influences on values; likewise, drama and reality genres also respectively produces differentiated impact on values and social trust. Drama viewing itself shows contrary patterns on civic attitude, i. e., positive relationship with social trust while negative with tolerance.

• Annual Conference on Human and Language Technology
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• 2009.10a
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• pp.145-150
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• 2009

이 논문은 한글 뉴스 기사의 댓글에 대한 감정 분류 방법을 제안한다. 제안된 방법은 기계학습을 이용하는데 본 논문에서는 자질의 가중치를 재조정하는 좀 색다른 방법을 제안한다. 일반적으로 댓글은 독자들이 특정 기사에 대해서 어떠한 감정을 가지고 있는지를 파악하는 중요한 단서가 된다. 그런데 독자들의 감정은 가사에 어떤 분야에 속하느냐에 영향을 받는다. 예를 들면 정치 기사는 부정적인 댓글은 많이 포함하고 있으며 인물 기사는 긍정적인 기사를 많이 포함한다. 이 논문은 이와 같은 댓글의 속성을 이용해서 기사의 원문과 기사의 분야 정보를 이용하여 가중치를 조정한다. 제안된 시스템의 성능을 평가하기 위해 신문 기사와 댓글을 수집하여 감정 말뭉치를 구축하였으며 감정자질을 추출하기 위해 감정 사전을 구축하였다. 제안된 시스템의 $F_1$ 척도는 92.2%였으며 원문의 감정 단어와 분야 정보가 댓글의 감정을 분류하는데 중요한 자질임을 알 수 있었다.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1257-1270
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• 2016

This study deals with the prediction of the total number of movie audiences as a measure for the box office. Prediction is performed by classification techniques of data mining such as decision tree, multilayer perceptron(MLP) neural network model, multinomial logit model, and support vector machine over time such as before movie release, release day, after release one week, and after release two weeks. Predictors used are: online word-of-mouth(OWOM) variables such as the portal movie rating, the number of the portal movie rater, and blog; in addition, other variables include showing the inherent properties of the film (such as nationality, grade, release month, release season, directors, actors, distributors, the number of audiences, and screens). When using 10-fold cross validation technique, the accuracy of the neural network model showed more than 90 % higher predictability before movie release. In addition, it can be seen that the accuracy of the prediction increases by adding estimates of the final OWOM variables as predictors.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.217-225
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• 2017

Recently, the interest in predicting hot topics has grown significantly as it has become more important to find and analyze meaningful information from a large amount of data flowing in social networking services. Existing hot topic detection schemes do not consider a temporal property, so they are not suitable to predict hot topics that are rapidly issued in a changing society. This paper proposes a hot topic prediction scheme that uses a modified TF-IDF in social networking environments. The modified TF-IDF extracts a candidate set of keywords that are momentarily issued. The proposed scheme then calculates the hot topic prediction scores by assigning weights considering user influence and professionality to extract the candidate keywords. The superiority of the proposed scheme is shown by comparing it to an existing detection scheme. In addition, to show whether or not it predicts hot topics correctly, we evaluate its quality with Korean news articles from Naver.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.10-27
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• 2018

The purpose of this study is to explore the meaning of happiness and unhappiness in Korean society through text mining analysis. Similar words with keywords(happiness/unhappiness) from online news portal are extracted using Word2Vec and TF-IDF method. We also use the K-LIWC dictionary to perform the sentiment analysis of words associated with happiness and unhappiness. In TF-IDF analysis, happiness and unhappiness are highly related to social factors and social issues of the year. In Word2Vec analysis, 'Hope' has been similar with happiness for six years. In K-LIWC analysis, 'money/financial issues', 'school', 'communication' is highly related with happiness and unhappiness. In addition, 'physical condition and symptom' is highly related to unhappiness. Implications, limitations, and suggestions for future research are also discussed.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.177-195
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• 2017

Traditionally, news was consumed only through printed newspapers and broadcasting media, such as radio and television. However, the Internet has enabled people to consume news content online. Since most of online news content has been provided for free, it is not easy for news providers to charge the fixed subscription fee for online news content. Therefore, as an alternative strategy, some online news providers have tried to adopt the Pay-What-You-Want (PWYW) pricing model, which allows users (readers) to pay as much as they want after consuming news content. As this pricing model shows some possibility to grow and replace the unsuccessful monetization strategy of online news content, we therefore examined the comparative importance of seven heuristic attributes (i.e., article evaluation, article share, article comment, article information design, article length, writer SNS, and writer information) affecting readers' voluntary payment behavior through a conjoint analysis with 379 news articles collected from online news Website (i.e., Ohmynews.com) where the PWYW model has been working successfully. This study found that article share and article length are the most important factors which affect online news content users' voluntary payment. Finally, two major and eight minor propositions are suggested based on the findings of the study. This study would suggest guidelines for how to create online news content which induces much more voluntary payment.

• The Journal of Korean Association of Computer Education
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• v.12 no.2
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• pp.61-75
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• 2009

Modern day people can easily use an e-mail client for general communication, because of using Internet and cellular phone. The mail client for the purpose of private and business affair, advertisement, news searching, and business letter is widely used and has side effects. People could send an important document via an electronic mail client. It is important to support an e-mail client intelligent. We think that many kinds of techniques of natural language processing must be provided in the client with human's emotion. We consider to design a new mail client with six kinds of senders' emotional information; delight, angry, sad feeling and message to express, manner of talking, a discomfort index etc. Before sending an e-mail, we suggest a user to correct a bad word because we do not want to feel bad to a receiver. We present a proper process of sending/receiving for users with a new designed e-mail clients.