An Ensemble Model for Cross-Domain Polarity Classification on Twitter

  • Adam Tsakalidis
  • Symeon Papadopoulos
  • Ioannis Kompatsiaris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8787)


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.


Sentiment analysis polarity detection ensemble classifier 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

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

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