  The task of learning a sentiment classification model that adapts well to any
target domain, different from the source domain, is a challenging problem.
Majority of the existing approaches focus on learning a common representation
by leveraging both source and target data during training. In this paper, we
introduce a two-stage training procedure that leverages weakly supervised
datasets for developing simple lift-and-shift-based predictive models without
being exposed to the target domain during the training phase. Experimental
results show that transfer with weak supervision from a source domain to
various target domains provides performance very close to that obtained via
supervised training on the target domain itself.
