Despotakis, D, Dimitrova, VG, Lau, L et al. (1 more author) (2013) Semantic aggregation and zooming of user viewpoints in social media content. In: Carberry, S, Weibelzahl, S, Micarelli, A and Semeraro, G, (eds.) User Modeling, Adaptation, and Personalization. 21st International Conference on User Modeling, Adaptation, and Personalization, 10-14 Jun 2013, Rome, Italy. Springer , 51 - 63 (13). ISBN 978-3-642-38843-9
Abstract
Social web provides rich content for gaining an understanding about the users which can empower adaptation. There is a current trend to extract user profiles from social media content using semantic augmentation and linking to domain ontologies. The paper shows a further step in this research strand, exploiting semantics to get a deeper understanding about the users by extracting the domain regions where the users focus, which are defined as viewpoints. The paper outlines a formal framework for extracting viewpoints from semantic tags associated with user comments. This enables zooming into the viewpoints at different aggregation layers, as well as comparing users on the basis of the areas where they focus. The framework is applied on YouTube content, illustrating an insight into emotions users refer to in their comments on job interview videos.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Keywords: | Social media content; User model representation and extraction; Viewpoints; YouTube; Adaptive learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Institute for Computational and Systems Science (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Sep 2014 11:51 |
Last Modified: | 19 Dec 2022 13:25 |
Published Version: | http://dx.doi.org/10.1007/978-3-642-38844-6_5 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-642-38844-6_5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:75831 |