Stretching the Life of Twitter Classifiers with Time-Stamped Semantic Graphs

  • Amparo Elizabeth Cano
  • Yulan He
  • Harith Alani
Conference paper

DOI: 10.1007/978-3-319-11915-1_22

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8797)
Cite this paper as:
Cano A.E., He Y., Alani H. (2014) Stretching the Life of Twitter Classifiers with Time-Stamped Semantic Graphs. In: Mika P. et al. (eds) The Semantic Web – ISWC 2014. ISWC 2014. Lecture Notes in Computer Science, vol 8797. Springer, Cham

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 Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated.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.

Keywords

social media topic detection DBpedia concept drift feature relevance decay 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Amparo Elizabeth Cano
    • 1
  • Yulan He
    • 2
  • Harith Alani
    • 1
  1. 1.Knowledge Media InstituteOpen UniversityUK
  2. 2.School of Engineering and Applied ScienceAston UniversityUK

Personalised recommendations