Recently, semi-supervised learning, which uses large amount of unlabeled data together with labeled data to build better learners (Raina et al., 2007; Zhu, 2007) , has drawn more attention in sentiment analysis<cite> (Dasgupta and Ng, 2009</cite>; Li, et al., 2009) . As argued by several researchers (Bengio, 2007; Salakhutdinov and Hinton, 2007) , deep architecture, composed of multiple levels of non-linear operations (Hinton et al., 2006) , is expected to perform well in semi-supervised learning because of its capability of modeling hard artificial intelligent tasks.