More Is Better: Large Scale Partially-supervised Sentiment Classification - Appendix
This work addresses sentiment classification for product reviews, showing incremental improvements through large-scale data utilization.
The authors tackled sentiment classification by developing a bootstrapping algorithm to learn from partially labeled data, using up to 15 million unlabeled Amazon product reviews, and in some cases reduced test error by more than half.
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.