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Evaluation of Different Strategies for Domain Adaptation in Opinion Mining

Abstract : The work presented in this article takes place in the field of opinion mining and aims more particularly at finding the polarity of a text by relying on machine learning methods. In this context, it focuses on studying various strategies for adapting a statistical classifier to a new domain when training data only exist for one or several other domains. This study shows more precisely that a self-training procedure consisting in enlarging the initial training corpus with unannotated texts from the target domain that were reliably classified by the classifier is the most successful and stable strategy for the tested domains. Moreover, this strategy gets better results in most cases than (Blitzer et al., 2007)'s method on the same evaluation corpus while it is more simple.
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Contributor : Marco Dinarelli <>
Submitted on : Wednesday, March 15, 2017 - 3:19:51 PM
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  • HAL Id : hal-01489996, version 1



Anne Garcia-Fernandez, Olivier Ferret, Marco Dinarelli. Evaluation of Different Strategies for Domain Adaptation in Opinion Mining. Language Resources Evaluation Conference (LREC), May 2014, Reykjavik, Iceland. ⟨hal-01489996⟩



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