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The Naive Bayes Classifier in Opinion Mining: In Search of the Best Feature Set

  • Liviu P. Dinu
  • Iulia Iuga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7181)

Abstract

This paper focuses on how naive Bayes classifiers work in opinion mining applications. The first question asked is what are the feature sets to choose when training such a classifier in order to obtain the best results in the classification of objects (in this case, texts). The second question is whether combining the results of Naive Bayes classifiers trained on different feature sets has a positive effect on the final results. Two data bases consisting of negative and positive movie reviews were used when training and testing the classifiers for testing purposes.

Keywords

Frequent Word Opinion Mining Sentiment Analysis Informative Feature Computational Linguistics 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Liviu P. Dinu
    • 1
  • Iulia Iuga
    • 1
  1. 1.Faculty of Mathematics and Computer Science, Center for Computational LinguisticsUniversity of BucharestBucharestRomania

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