Evaluation of Different Strategies for Domain Adaptation in Opinion Mining - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

Evaluation of Different Strategies for Domain Adaptation in Opinion Mining

Résumé

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.
Fichier principal
Vignette du fichier
LREC2014.pdf (169.32 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-01489996 , version 1 (15-03-2017)

Identifiants

  • HAL Id : hal-01489996 , version 1

Citer

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⟩
185 Consultations
250 Téléchargements

Partager

Gmail Facebook X LinkedIn More