More Is Better: Large Scale Partially-Supervised Sentiment Classification
Abstract
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.
Cite
Text
Haimovitch et al. "More Is Better: Large Scale Partially-Supervised Sentiment Classification." Proceedings of the Fourth Asian Conference on Machine Learning, 2012.Markdown
[Haimovitch et al. "More Is Better: Large Scale Partially-Supervised Sentiment Classification." Proceedings of the Fourth Asian Conference on Machine Learning, 2012.](https://mlanthology.org/acml/2012/haimovitch2012acml-more/)BibTeX
@inproceedings{haimovitch2012acml-more,
title = {{More Is Better: Large Scale Partially-Supervised Sentiment Classification}},
author = {Haimovitch, Yoav and Crammer, Koby and Mannor, Shie},
booktitle = {Proceedings of the Fourth Asian Conference on Machine Learning},
year = {2012},
pages = {175-190},
volume = {25},
url = {https://mlanthology.org/acml/2012/haimovitch2012acml-more/}
}