• Journal of the Korean Society for Library and Information Science
• /
• v.51 no.3
• /
• pp.45-64
• /
• 2017

The purpose of this study is to understand the direction of the community reading campaign in the U.S.A. known as 'One Book, One City' reflected in the books selected for this campaign for the past 20 years in terms of their classification numbers, subject headings, publication dates, and genres. Analyzed are the author and state lists of 'One Book, One City' Reading Promotions Projects available from the website of the LC (Library of Congress) Center for the Books, and bibliographic records of 735 books selected in only one 'One Book' program, accessed from LC OPAC. Major findings include continuing influences of the all-time favorite 'One Book' selections, including To Kill a Mockingbird and the extension of their span of life through The Big Read, preference for the recent publications, importance of P (Literatures and Languages) Class (530 titles, 72.1%) and PS(American Literatures) subclass (307 titles, 57.9%) in the LC Classification Scheme, distribution of books in 43 genres, including domestic fiction, historical fiction, and psychological fiction, etc., the use of 535 unique LC subject headings and much interests in "City and town life" (10 titles) and "World War, 1939-1945" (8 titles), and prominence of subject groups which begin with "African American..." and "Woman..." out of 96 groups of subject headings. It is found that the subjects and focus of the selected books expand from integration, understanding, integrity to human rights, environment, peace, etc. The limitations of this study is that the influence of the selected books and the changes in communities are not properly analyed.

• Journal of the Korean Society for Library and Information Science
• /
• v.43 no.1
• /
• pp.5-26
• /
• 2009

The purpose of this study is to improve the classification focused on KDC 4th edition for children's materials. For the study, literature study, KDC application and user survey in 51 libraries for children were performed. Based on the results, classification for children's materials is proposed to improve in two ways: classification system itself and arrangement in the shelf. First of all, it is required to scatter the subjects that are centered on specific numbers and to allow various types, characteristics and literature genre of children's materials to be classified in KDC. KDC also should provide specific notes and classification guidelines for interdisciplinary subjects. In the shelf arrangement, it is proposed to consider the introduction of user-friendly call number and arrangement principles by ages or titles. In the end, it is necessary to compose various collections that represent users' needs, and the user education program is needed.

• Journal of Korea Multimedia Society
• /
• v.10 no.5
• /
• pp.559-568
• /
• 2007

Anchor-person scene detection is of significance for video shot semantic parsing and indexing clues extraction in content-based news video indexing and retrieval system. This paper proposes an efficient algorithm extracting anchor ranges that exist in sports news video for unit structuring of sports news. To detect anchor person scenes, first, anchor person candidate scene is decided by DCT coefficients and motion vector information in the MPEG4 compressed video. Then, from the candidate anchor scenes, image processing method is utilized to classify the news video into anchor-person scenes and non-anchor(sports) scenes. The proposed scheme achieves a mean precision and recall of 98% in the anchor-person scenes detection experiment.

• Journal of Korea Game Society
• /
• v.21 no.4
• /
• pp.73-84
• /
• 2021

In this paper, we propose an analysis method that categorizes the Core that essential behaviors in game, the Primary that solves the game problem, and the Secondary that helps the Core and the Primary. The proposed method can analyze the genre similarity and characteristics of the game, the richness of the content, and the proficiency level of the game. case study were conducted to confirm whether the analysis items were consistent with the objective game experience. The results of this study are expected to be helpful in improving game design ability.

• Smart Media Journal
• /
• v.9 no.3
• /
• pp.90-97
• /
• 2020

With the development of the Internet and smartphones, digital sound sources are easily accessible, and accordingly, interest in music search and recommendation is increasing. As a method of recommending music, research using melodies such as pitch, tempo, and beat to classify genres or emotions is being conducted. However, since lyrics are becoming one of the means of expressing human emotions in music, the role of the lyrics is increasing, so a study of emotion classification based on lyrics is needed. Therefore, in this thesis, we analyze the emotions of the farewell lyrics in order to subdivide the farewell emotions based on the lyrics. After constructing an emotion dictionary by vectoriziong the similarity between words appearing in the parting lyrics through Word2Vec learning, we propose a method of classifying parting lyrics emotions using Word2Vec and LSTM, which classify lyrics by similar emotions by learning lyrics using LSTM.

• Journal of the Korean Society for information Management
• /
• v.25 no.1
• /
• pp.5-17
• /
• 2008

The purpose of this paper is to identify the usefulness of Weblog genre analysis in knowledge creation within an organization where communications are occurred frequently among the employees with information and communication technologies (ICTs). Knowledge creation is essential to achieve competitive advantage in today's knowledge-oriented working environments. There has been huge investment on knowledge management systems to achieve such advantages. It is, however, widely recognized that distributed knowledge management systems often fail due to the different social contexts across the sub-organizations where the local information systems are deployed. It is important to coordinate such social gaps across the sub-organizations to achieve better advantages. Given that Weblogs users often feel a shared social norm. Weblogs playa positive role of narrowing the social gaps. This paper argues that the genre analysis of Web logs could provide important clues to narrow the social gaps existed across the sub-organizations within an organization. Hence, such taxonomical practice may be a solution for the high rate of failure in knowledge management system implementations in an organization. This paper uses the theory of organizational knowledge creation (Nonaka & Takeuchi. 1995) and social construction of technology (SCOT) approach (Bijker, 1995) to compare the socialized with the non-socialized integration of distributed information systems. The findings of this research provide a useful framework for better implementing knowledge management practices especially in distributed working environments.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2015.01a
• /
• pp.51-54
• /
• 2015

추천 시스템의 대표적인 연구 중 하나인 콘텐츠 기반 추천 시스템 연구는 TV 프로그램이나 영화의 줄거리, 장르, 리뷰 등의 콘텐츠의 메타데이터를 이용한다. 그러나 이러한 연구들은 콘텐츠 관련 정보에만 의존할 뿐, 시청자의 프로파일과 콘텐츠의 정보를 함께 고려하지 않는다. 본 논문에서는 시청자의 프로파일 중 연령과 콘텐츠의 정보인 프로그램의 줄거리를 활용한 TV 프로그램 추천 시스템을 제안한다. 본 추천 시스템은 시청자를 연령에 따라 분류한 후, LDA 알고리즘을 이용하여 시청자의 시청 TV 프로그램의 줄거리를 분류된 나이에 따라 각각의 줄거리 토픽 모델로 생성한다. 이를 기준으로 시청자가 원하는 시간대에 방송되는 프로그램들의 줄거리 토픽벡터와 시청자의 선호도 토픽벡터의 유사도를 비교해 가장 유사도가 높은 TV 프로그램을 시청자에게 추천하는 방식이다. 본 논문에서는 연구의 효용성을 검증하기 위해 줄거리만을 사용한 경우와 줄거리와 연령을 동시에 활용한 경우를 비교 실험하였다. 실험을 통해 프로그램의 줄거리만을 사용한 경우보다 연령을 동시에 활용한 경우의 추천 시스템 성능이 개선된 것을 확인할 수 있었다.

• Smart Media Journal
• /
• v.9 no.2
• /
• pp.69-77
• /
• 2020

Unlike the past, which was limited to terrestrial broadcasts, many dramas are currently being broadcast on cable channels and the Internet web. After watching the drama, viewers actively express their opinions through reviews and studies related to the analysis of these reviews are actively being conducted. Due to the nature of the drama, the genre is not clear, and due to the various age groups of viewers, reviews and ratings from other viewers help to decide which drama to watch. However, since it is difficult for viewers to check and analyze many reviews individually, a data analysis technique is required to automatically analyze them. Accordingly, this paper classifies the topics of reviews that have an important influence on drama selection and reclassifies them into semantic topics according to the similarity of words. In addition, we propose a model that classifies reviews into sentences according to semantic topics and sentiment analysis through sentiment words.

• Journal of the Korean Society for Library and Information Science
• /
• v.49 no.4
• /
• pp.179-201
• /
• 2015

The subject heading is an elaborate access tool for subject browsing and searching in information retrieval environment. The purpose of this study is to suggest the applicable facets to subject headings in Korea. First, the concepts of subject and the definitions of facets were investigated in the literature review. Second, six cases including OCLC's FAST, PRECIS, "Thesaurus construction and use", CC $7^{th}$ edition, BC $2^{nd}$ Edition, and UDC $3^{rd}$ Edition were analyzed to focus on configuration of facets as case studies. Based on the results, twenty-two facets were proposed including Topical, Event, Geography, Chronology, Personal and Corporate Name, Title, Form, Genre, Language, and Person facets as 11 top facets. Also, Topical-Thing/Entity and Topical-Action/Status, Part, Kind, Property, Whole, Material, Patient, Product, By-Product and Agent facets as sub-facets of Topical facet.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.4
• /
• pp.877-885
• /
• 2011

A collaborative filtering(CF) is the most widely used technique in recommender system. However, CF has sparsity and scalability problems. These problems reduce the accuracy of recommendation and extensive studies have been made to solve these problems, In this paper, we proposed a method that uses a weight so as to solve these problems. After creating a user-item matrix, the proposed method analyzes information about users who prefer the item only by using data with a rating over 4 for enhancing the accuracy in the recommendation. The proposed method uses information about the genre of the item as well as analyzed user information as a weight during the calculation of similarity, and it calculates prediction by using only data for which the similarity is over a threshold and uses the data as the rating value of unrated data. It is possible simultaneously to reduce sparsity and to improve accuracy by calculating prediction through an analysis of the characteristics of an item. Also, it is possible to conduct a quick classification based on the analyzed information once a new item and a user are registered. The experiment result indicated that the proposed method has been more enhanced the accuracy, compared to item based, genre based methods.