Evaluation of Different Strategies for Domain Adaptation in Opinion Mining

• Main Authors: Garcia-Fernandez, Anne, Ferret, Olivier, Dinarelli, Marco
• Other Authors: Laboratoire d'anthropologie sociale (LAS), École des hautes études en sciences sociales (EHESS)-Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Département Intelligence Ambiante et Systèmes Interactifs (DIASI (CEA, LIST)), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL), Université Sorbonne Paris Cité (USPC), Lattice - Langues, Textes, Traitements informatiques, Cognition - UMR 8094 (Lattice), Université Sorbonne Nouvelle - Paris 3-Université Sorbonne Paris Cité (USPC)-Centre National de la Recherche Scientifique (CNRS)-Université Paris sciences et lettres (PSL)-Département Littératures et langage - ENS Paris (LILA), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2014
• Subjects: Opinion mining; domain adaptation; self-training; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; Iceland
• Online Access: https://hal.science/hal-01489996; https://hal.science/hal-01489996/document; https://hal.science/hal-01489996/file/LREC2014.pdf

International audience 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.