Evaluation of different strategies for domain adaptation in opinion mining

Anne Garcia-Fernandez, Olivier Ferret, Marco Dinarelli

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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
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
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:
Cite (ACL):
Anne Garcia-Fernandez, Olivier Ferret, and Marco Dinarelli. 2014. Evaluation of different strategies for domain adaptation in opinion mining. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3877–3880, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Evaluation of different strategies for domain adaptation in opinion mining (Garcia-Fernandez et al., LREC 2014)
Copy Citation:
PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/617_Paper.pdf