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Stretching the life of Twitter classifiers with time-stamped semantic graphs

Cano, Amparo Elizabeth; He, Yulan and Alani, Harith (2014). Stretching the life of Twitter classifiers with time-stamped semantic graphs. In: The Semantic Web – ISWC 2014: Proceedings, Part II, Springer International Publishing, pp. 341–357.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-11915-1_22
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Abstract

Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

Item Type: Conference or Workshop Item
Copyright Holders: 2014 Springer International Publishing Switzerland
ISBN: 3-319-11914-1, 978-3-319-11914-4
ISSN: 0302-9743
Keywords: social media; topic detection; DBpedia; concept drift; feature relevance decay
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 41402
Depositing User: Amparo Cano Basave
Date Deposited: 25 Nov 2014 09:57
Last Modified: 12 Sep 2018 22:31
URI: http://oro.open.ac.uk/id/eprint/41402
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