Train a sentiment classifier on S * L ∪ T L and use it to classify target domain test instances.
recent work on UDA (Morerio et al., 2018) has shown that minimising the entropy of a classifier on its predictions in the source and target domains is equivalent to learning a projection space that maximises the correlation between source and target instances.