Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/16526
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Sentic Web: A New Paradigm for Managing Social Media Affective Information
Authors: Grassi, Marco
Cambria, Erik
Hussain, Amir
Piazza, Francesco
Contact Email: amir.hussain@stir.ac.uk
Keywords: Sentic computing
AI
Semantic web
Ontologies
NLP
Emotion and affective UI
Issue Date: Sep-2011
Publisher: Springer
Citation: Grassi M, Cambria E, Hussain A & Piazza F (2011) Sentic Web: A New Paradigm for Managing Social Media Affective Information, Cognitive Computation, 3 (3), pp. 480-489.
Abstract: The recent success of media-sharing services caused an exponential growth of community-contributed multimedia data on the Web and hence a consistent shift of the flow of information from traditional communication channels to social media ones. Retrieving relevant information from this kind of data is getting more and more difficult, not only for their volume, but also for the different nature and formats of their contents. In this work, we introduce Sentic Web, a new paradigm for the management of social media affective information, which exploits AI and Semantic Web techniques to extract, encode, and represent opinions and sentiments over the Web. In particular, the computational layer consists in an intelligent engine for the inference of emotions from text, the representation layer is developed on the base of specific domain ontologies, and the application layer is based on the faceted browsing paradigm to make contents available as an interconnected knowledge base.
Type: Journal Article
URI: http://hdl.handle.net/1893/16526
DOI Link: http://dx.doi.org/10.1007/s12559-011-9101-8
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Università Politecnica delle Marche
University of Stirling
Computing Science - CSM Dept
Università Politecnica delle Marche

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