  We describe a bootstrapping algorithm to learn from partially labeled data,
and the results of an empirical study for using it to improve performance of
sentiment classification using up to 15 million unlabeled Amazon product
reviews. Our experiments cover semi-supervised learning, domain adaptation and
weakly supervised learning. In some cases our methods were able to reduce test
error by more than half using such large amount of data.
  NOTICE: This is only the supplementary material.
