Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/16505
Appears in Collections:Computing Science and Mathematics Book Chapters and Sections
Peer Review Status: Refereed
Title: Clustering social networks using interaction semantics and sentics
Authors: Chandra, Praphul
Cambria, Erik
Hussain, Amir
Contact Email: amir.hussain@stir.ac.uk
Editors: Wang, J
Yen, GG
Polycarpou, MM
Citation: Chandra P, Cambria E & Hussain A (2012) Clustering social networks using interaction semantics and sentics. In: Wang J, Yen GG, Polycarpou MM (ed.). Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part I. Lecture Notes in Computer Science, 7367, Berlin Heidelberg: Springer, pp. 379-385.
Keywords: Social Network Analysis
Sentic Computing
NLP
Issue Date: 2012
Publisher: Springer
Series/Report no.: Lecture Notes in Computer Science, 7367
Abstract: The passage from a static read-only Web to a dynamic read-write Web gave birth to a huge amount of online social networks with the ultimate goal of making communication easier between people with common interests. Unlike real world social networks, however, online social groups tend to form for extremely varied and multi-faceted reasons. This makes very difficult to group members of the same social network in subsets in a way that certain types of contents are shared with just certain types of friends. Moreover, such a task is usually too tedious to be performed manually and too complex to be performed automatically. In this work, we propose a new approach for automatically clustering social networks, which exploits interaction semantics and sentics, that is, the conceptual and affective information associated with the interactive behavior of online social network members.
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.
Type: Part of book or chapter of book
URI: http://hdl.handle.net/1893/16505
URL: http://link.springer.com/chapter/10.1007/978-3-642-31346-2_43#
Affiliation: Hewlett Packard
National University of Singapore
Computing Science - CSM Dept

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