• Cartoon and Animation Studies
• /
• s.44
• /
• pp.285-306
• /
• 2016

With the increase use of smartphones, users can consume contents such as webtoon, webnovel and TV drama directly provided by the producers. In this Direct-to-Consumer era, webdrama services from the portal websites are increasing rapidly. Webdramas such as , , and can be analyzed in real time using responses such as unique users, likes, and comments. The analyses used in this research were Social Media Big Data Mining Method and Opinion Mining Method. Specific key words from webdrama can be extracted and viewers positive, neutral or negative emotion can be predicted from the words. The analyses of popular webdramas showed that the established K-Pop Idol member appearance and servicing portal site greatly influence the views, traffics, comments, and likes. Also, 'Mobile TV' proved the effectiveness as another platform other than television. Mobile targeted contents and robust business models still to be developed and identified. Overcoming these few tasks, Korea will be proven to be a webdrama content powerhouse.

• Digital Contents
• /
• no.12 s.79
• /
• pp.28-29
• /
• 1999

데이터 마이닝을 통해 기업은 웹사이트상의 패턴을 의미 있는 정보로 종합해내고 인터넷 상의 고객들과 예상치를 이해하고 연관시킬수 있게 된다. 데이터와 웹이 제공하는 방대한 사업지식의 흐름에 근거한 웹 마이닝은 온라인 고객과의 관계를 생성하고 유지시키며 생산성 있는 온라인 상점의 최전선을 구축하는데 있어 결정적 열쇠가 되는 것이다.

• Proceedings of the Korea Contents Association Conference
• /
• 2006.11a
• /
• pp.16-20
• /
• 2006

In Web contents mining, it is important to recognize the unlabeled entities and to integrate the sub-linked information and the extracted results. This paper presents the probabilistic based method which can recognize the unlabeled entity by using the Baysien model. Moreover, we propose the method that can use the information of the sub-linked web pages and integrate the extracted results. In the experimental results, we can see that the probabilistic based entity and information integration show the most significant precision.

• The Journal of the Korea Contents Association
• /
• v.11 no.10
• /
• pp.59-68
• /
• 2011

As the role of soccer game analyst who analyzes soccer games and creates soccer wining strategies is emphasized, it is required to have high-level analysis beyond the procedural ones such as main event detection in the context of IT based broadcasting soccer game research community. In this paper, we propose a novel approach to generate the high-level in-depth analysis results via real-time text based soccer Webcast and text mining. Proposed method creates a metadata such as attribute, action and event, build index, and then generate available knowledges via text mining techniques such as association rule mining, event growth index, and pathfinder network analysis using Webcast and domain knowledges. We carried out a feasibility experiment on the proposed technique with the Webcast text about Spain team's 2010 World Cup games.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.04a
• /
• pp.99-102
• /
• 2007

웹 사용 마이닝은 사용자의 웹 이용 패턴에 대해 분석하여 정보를 찾아내는 분야이다. 사용자에 대한 분석은 웹을 통한 비즈니스의 근간이 되고 있다. 때문에 웹 마이닝 분야에서 주목받고 중요시 되는 기술이 되었다. 그러나 최근에는 공개된 기술의 취약점을 이용해 악의적으로 정보를 교란하는 일이 발생되고 있어 사회적으로 이슈가 되고 있다. 이러한 문제는 특히 단순한 페이지 뷰 횟수에 기반을 둔 정보 추출 방식에 주로 발생하고 있다. 따라서 본 논문에서는 이러한 추출 방식의 단순함을 줄이고 사용자의 정보를 더 반영하기 위하여 페이지 이용 시간과 페이지 내의 행동을 분석하여 콘텐츠의 질을 평가하는 방안을 제시한다. 구현 부분에는 사용자의 개인정보 침해 없이 사용자의 행동을 수집하기 위하여 최근 인기를 얻고 있는 Ajax 기술을 사용하였다. 그리고 실시간으로 웹 페이지에 대한 평가를 수행하기 위해 서버에 로그 필터 모듈을 추가하는 수집 기법을 제안하였다.

• Proceedings of the Korea Contents Association Conference
• /
• 2018.05a
• /
• pp.361-362
• /
• 2018

본 연구는 대학 웹페이지의 Q&A(질의응답) 게시판에 게재된 비정형화 된 데이터를 수집한 후 텍스트마이닝과 네트워크 분석을 활용하여 자주 등장하는 키워드 간 연관 패턴을 파악하고자 한다. 분석결과를 바탕으로 FAQ(자주하는 질문) 게시판을 구성한다면 반복적인 질문에 대한 민원을 간소화함으로써 수요자의 편의성과 행정의 효율성 향상에 기여하고 나아가 원활한 양방향 소통이 가능할 것으로 기대한다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.1
• /
• pp.68-79
• /
• 2011

This paper focuses on the opinion information extraction and analysis system through Web mining that is based on statistics collected from Web contents. That is, users' opinion information which is scattered across several websites can be automatically analyzed and extracted. The system provides the opinion information search service that enables users to search for real-time positive and negative opinions and check their statistics. Also, users can do real-time search and monitoring about other opinion information by putting keywords in the system. Proposing technique proved that the actual performance is excellent by comparison experiment with other techniques. Performance evaluation of function extracting positive/negative opinion information, the performance evaluation applying dynamic window technique and tokenizer technique for multilingual information retrieval, and the performance evaluation of technique extracting exact multilingual phonetic translation are carried out. The experiment with typical movie review sentence and Wikipedia experiment data as object as that applying example is carried out and the result is analyzed.

• Annual Conference of KIPS
• /
• 2014.11a
• /
• pp.1050-1053
• /
• 2014

웹 기반 서비스의 발전과 스마트 기기의 보급으로 사용자들은 다양한 웹 서비스들을 이용할 수 있게 되었고, 소셜 웹과 같은 사람들 간의 관계를 형성함으로써 정보를 주고받는 서비스에 접근하여 자신만의 콘텐츠를 생성, 공유하기가 용이해졌다. 그러나 소셜 웹 사용자들이 증가하고 지식의 양이 늘어남에 따라, 방대한 양의 지식들 중 필요한 정보만을 효율적으로 창출해내고자 하는 연구 또한 시도되어 왔다. 그러나, 기존의 방법은 다수의 서비스 사용자들의 공통적인 관심사가 반영된 결과를 도출해내기에는 부족하다는 단점이 있었다. 그리하여, 본 논문에서는 집단 지성 알고리즘과 의사 결정 나무를 활용하여 소셜 웹을 이용하는 사용자들의 태그와 URL 정보를 토대로 트렌드를 분석, 콘텐츠를 추천하는 방법을 제안하고, 이를 통하여 다수 사용자들의 기호가 반영된 다양한 정보들을 소셜 웹 사용자들에게 제공해줄 수 있음을 보인다.

• Journal of KIISE:Software and Applications
• /
• v.34 no.6
• /
• pp.539-548
• /
• 2007

Mobile content adaptation is a technology of effectively representing the contents originally built for the desktop PC on wireless mobile devices. Previous approaches for Web content adaptation are mostly device-dependent. Also, the content transformation to suit to a smaller device is done manually. Furthermore, the same contents are provided to different users regardless of their individual preferences. As a result, the user has difficulty in selecting relevant information from a heavy volume of contents since the context information related to the content is not provided. To resolve these problems, this paper proposes an enhanced method of Web content adaptation for mobile devices. In our system, the process of Web content adaptation consists of 4 stages including block filtering, block title extraction, block content summarization, and personalization through learning. Learning is initiated when the user selects the full content menu from the content summary page. As a result of learning, personalization is realized by showing the information for the relevant block at the top of the content list. A series of experiments are performed to evaluate the content adaptation for a number of Web sites including online newspapers. The results of evaluation are satisfactory, both in block filtering accuracy and in user satisfaction by personalization.

• Journal of Digital Contents Society
• /
• v.9 no.2
• /
• pp.251-260
• /
• 2008

In internet being a repository of massive information, we easily may not find our desired information, this issue also exists in e-commerce which gets rapid growth. In most of e-commerce sites, the methods furnishing information have been made use of statistical analysis or simple process by category-oriented, but these can't represent diverse correlation among products information and also hardly reflect users' purchasing patterns precisely. In this thesis, we propose more efficient web mining ways for discovering interesting information using effective clustering in e-commerce, which get achieved more suitable relationship among products information using both sequential patterns and association rules in category-independent, and experiments show the efficiency of our proposed methods. And we propose search using effective clustering rapidly.