An Ensemble Model for Cross-Domain Polarity Classification on Twitter

  • Adam Tsakalidis
  • Symeon Papadopoulos
  • Ioannis Kompatsiaris
Conference paper

DOI: 10.1007/978-3-319-11746-1_12

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8787)
Cite this paper as:
Tsakalidis A., Papadopoulos S., Kompatsiaris I. (2014) An Ensemble Model for Cross-Domain Polarity Classification on Twitter. In: Benatallah B., Bestavros A., Manolopoulos Y., Vakali A., Zhang Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8787. Springer, Cham

Abstract

Polarity analysis of Social Media content is of significant importance for various applications. Most current approaches treat this task as a classification problem, demanding a labeled corpus for training purposes. However, if the learned model is applied on a different domain, the performance drops significantly and, given that it is impractical to have labeled corpora for every domain, this becomes a challenging task. In the current work, we address this problem, by proposing an ensemble classifier that is trained on a general domain and and adapts, without the need for additional ground truth, on the desired (test) domain before classifying a document. Our experiments are performed on three different datasets and the obtained results are compared with various baselines and state-of-the-art methods; we demonstrate that our model is outperforming all out-of-domain trained baseline algorithms, and that it is even comparable with different in-domain classifiers.

Keywords

Sentiment analysis polarity detection ensemble classifier 

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

  • Adam Tsakalidis
    • 1
  • Symeon Papadopoulos
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Information Technologies InstituteCERTHThessalonikiGreece

Personalised recommendations