  The problem statement addressed in this work is : For a public sentiment
classification API, how can we set up a classifier that works well on different
types of data, having limited ability to annotate data from across domains. We
show that given a large amount of unannotated data from across different
domains and pseudolabels on this dataset generated by a classifier trained on a
small annotated dataset from one domain, we can train a sentiment classifier
that generalizes better across different datasets.
