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 model-based transfer learning solution---using linear transformations over a generic model---for personalized opinion analysis. Experimental results on a large collection of Amazon reviews confirm our method significantly outperforms a user-independent generic model as well as several state-of-the-art transfer learning algorithms.