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2012 ACML ACML 2012

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.

🌱 Topic Pioneer — Sentiment Analysis
🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
📈 Trend Setter — Sentiment Analysis
🧭 Keyword Pioneer — bootstrapping algorithm
🐣 Hot Topic Early Bird — semi-supervised learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio

Authors

Yoav Haimovitch , Koby Crammer , Shie Mannor

Topics

Machine Learning > Learning Types > Semi-Supervised Learning Machine Learning > Learning Types > Weakly Supervised Learning Natural Language Processing > Understanding > Sentiment Analysis Natural Language Processing > Applications > Sentiment Analysis Machine Learning > Learning Paradigms > Semi-Supervised Learning

Keywords

semi-supervised learning domain adaptation weakly supervised learning sentiment classification bootstrapping algorithm
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