Semi-supervised methods, which make use of both labeled and unlabeled data, are ideal for sentiment classification, since the cost of labeling data is high whereas unlabeled data are often readily available or easily obtained (Ortigosa-Hernández et al., 2012).
there are some drawbacks of semi-supervised approaches such as most of the work assume that the positive and negative samples in both labeled and unlabeled data set are balanced, otherwise models often bias towards the majority class (Chawla et al., 2002;Yen and Lee, 2009).