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TALN 2012

Study of various strategies for adapting an opinion classifier to a new domain

Keywords: Opinion Mining, Iterative Learning

(Short paper)

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 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, ACL2007)'s method on the same evaluation corpus while it is more simple.

Associated documents :
Garcia-Fernandez_al_TALN2012 (fr) - 226 Ko - Wed, Jun 06 2012
Poster Garcia-Fernandez&al_TALN2012 (fr) - 751 Ko - Wed, Jun 06 2012
Booster Garcia-Fernandez&al_TALN2012 (fr) - 864 Ko - Wed, Jun 06 2012

Associated news:
Paper at TALN 2012 - Fri, Apr 20 2012

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