소셜 뉴스를 위한 시간 종속적인 메타데이터 기반의 컨텍스트 공유 프레임워크

다국어 초록 (Multilingual Abstract)

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on users reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

참고문헌 (Reference)

1 Pedersen, T., "WordNet::Similarity : measuring the relatedness of concepts" Association for Computational Linguistics 38-41, 2004

2 Oh, J. H., "Why question answering using sentiment analysis and word classes" Association for Computational Linguistics 368-378, 2012

3 Wu, Z., "Verb semantics and lexical selection" 133-138, 1994

4 Misra, H., "Tv news story segmentation based on semantic coherence and content similarity" 5916 : 347-357, 2010

5 Hearst, M. A., "TextTiling : Segmenting text into multi-paragraph subtopic passages" 23 (23): 33-64, 1997

6 Lee, L. S., "Spoken document understanding and organization" 22 (22): 42-60, 2005

7 Stokes, N., "SeLeCT : a lexical cohesion based news story segmentation system" 17 (17): 3-12, 2004

8 Park, S.-H., "SNS News communicating" 49 (49): 37-73, 2012

9 Yoon, S.-H., "Revolution of Social TV" ebizbooks 2012

10 Cesar, P., "Past, present, and future of social TV : A categorization" 347-351, 2011

11 Xie, L., "Laplacian eigenmaps for automatic story segmentation of broadcast news" 20 (20): 276-289, 2012

12 Miller, G. A., "Introduction to wordnet : An on-line lexical database" 3 (3): 235-244, 1990

13 Pedersen, T., "Information content measures of semantic similarity perform better without sense-tagged text" Association for Computational Linguistics 329-332, 2010

14 Cunningham, H., "GATE, a general architecture for text engineering" 36 (36): 223-254, 2002

15 Sack, H., "Exploratory Semantic Video Search with yovisto" 446-447, 2010

16 Budanitsky, A., "Evaluating wordnet-based measures of lexical semantic relatedness" 32 (32): 13-47, 2006

17 Mihalcea, R., "Corpus-based and knowledge-based measures of text semantic similarity" 6 : 775-780, 2006

18 R. Malik, "Automatically Selecting Answer Templates to Respond to Customer Emails" 7 : 1659-1664, 2007

19 Sack, H., "Automated annotations of synchronized multimedia presentations" 2006

20 Jung, Y.-C., "2012 Broadcast Media Usage Patterns Research" Korea communications commission 2012