A Case-Based Approach to Cross Domain Sentiment Classification

  • Bruno Ohana
  • Sarah Jane Delany
  • Brendan Tierney
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7466)


This paper considers the task of sentiment classification of subjective text across many domains, in particular on scenarios where no in-domain data is available. Motivated by the more general applicability of such methods, we propose an extensible approach to sentiment classification that leverages sentiment lexicons and out-of-domain data to build a case-based system where solutions to past cases are reused to predict the sentiment of new documents from an unknown domain. In our approach the case representation uses a set of features based on document statistics, while the case solution stores sentiment lexicons employed on past predictions allowing for later retrieval and reuse on similar documents. The case-based nature of our approach also allows for future improvements since new lexicons and classification methods can be added to the case base as they become available. On a cross domain experiment our method has shown robust results when compared to a baseline single-lexicon classifier where the lexicon has to be pre-selected for the domain in question.


case-based reasoning sentiment classification sentiment lexicons 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bruno Ohana
    • 1
  • Sarah Jane Delany
    • 1
  • Brendan Tierney
    • 1
  1. 1.Dublin Institute of TechnologyDublinIreland

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