Evaluation of different strategies for domain adaptation in opinion mining

Anne Garcia-Fernandez, Olivier Ferret, Marco Dinarelli


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 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.
Anthology ID:
L14-1494
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3877–3880
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/617_Paper.pdf
DOI:
Bibkey:
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/617_Paper.pdf