String Kernels for Polarity Classification: A Study Across Different Languages

  • Rosa M. Giménez-Pérez
  • Marc Franco-Salvador
  • Paolo Rosso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10859)


The polarity classification task has as objective to automatically deciding whether a subjective text is positive or negative. Using a cross-domain setting implies the use of different domains for the training and testing. Recently, string kernels, a method which does not employ domain adaptation techniques has been proposed. In this work, we analyse the performance of this method across four different languages: English, German, French and Japanese. Experimental results show the strong potential of this approach independently from the language.


String kernels Sentiment analysis Single-domain Cross-domain 



The work of the third author was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rosa M. Giménez-Pérez
    • 1
  • Marc Franco-Salvador
    • 2
  • Paolo Rosso
    • 1
  1. 1.Universitat Politècnica de ValènciaValenciaSpain
  2. 2.Symanto ResearchNurembergGermany

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