Model Adaptation for Personalized Opinion Analysis

Humans are idiosyncratic and variable: towards the same topic, they might hold different opinions or express the same opinion in various ways. It is hence important to model opinions at the level of individual users; however it is impractical to estimate independent sentiment classification models for each user with limited data. In this paper, we adopt a modelbased transfer learning solution – using linear transformations over the parameters of a generic model – for personalized opinion analysis. Extensive experimental results on a large collection of Amazon reviews confirm our method significantly outperformed a user-independent generic opinion model as well as several state-ofthe-art transfer learning algorithms.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Mohammad Al Boni, Keira Zhou, Hongning Wang, Matthew S. Gerber
• Keywords: ACL 2015, Computational Linguistics